Computer Aided Detection

This paper presents a method for wavelet processing of mammogram images in order to highlight pathological changes which are important for diagnosing breast cancer. The method was used at the image pre-processing stage in a CAD (Computer Aided Detection) system which is under development. The paper presents the preliminary detection results for pathological areas with and without the method under discussion.

This paper tackles the challenge of interactively retrieving visual scenes within surveillance sequences acquired with fixed camera. Contrarily to today's solutions, we assume that no a-priori knowledge is available so that the system must progressively learn the target scenes thanks to interactive labelling of a few frames by the user. The proposed method is based on very low-cost features extraction and integrates relevance feedback, multiple-instance SVM classification and active learning. Each of these 3 steps runs iteratively over the session, and takes advantage of the progressively increasing training set. Repeatable experiments on both simulated and real data demonstrate the efficiency of the approach and show how it allows reaching high retrieval performances.

Summary Breast cancer continues to be a significant public health problem among women around the world. It has become the number one cause of cancer deaths amongst Malaysian women. The key to improve the breast cancer prognosis is by early detection. The important sign for the breast cancer detection is the presence of lesion such as microcalcification clusters (MCCs). In

Breast cancer is the most widespread cancer in women. The life-time risk of a woman developing this disease has been established as one in eight. Currently mammography is a standard method and could decrease breast cancer mortality. Unfortunately, negative mammograms don't exclude cancer. The sensitivity of mammography ranges from approximately 70% to 90% and it should be higher. MATERIALS AND METHODS: The sample contained 255 cases taken from Imaging Center of Imam Khomaini Hospital. Bilateral mammograms in both craniocaudal and mediolateral oblique projections were used. Two experienced Radiologists reviewed images before and after using CAD system. Tumors (including malignant and benign) and normal breast tissues were confi rmed by histological correlation. RESULTS: Of 255 cases 92 were not recommended for further work-up. Of 163 cases 90 were normal mass, 23 malignant tumors, 16 benign tumors and 22 cysts were detected by CAD system. The remaining cases were fi nalized only by biopsy. CONCLUSION: CAD could be utilized for breast mass detection. This is a practical technique with low cost.

The proposed method focuses on the prediction of breast cancer in its early stage. It is very difficult to detect microcalcification due to its small size and low contrast with respect to the surrounding tissues. A new, fast and simple enhancement technique is considered for early detection of breast cancer by enhancing microcalcification features using morphology and LOG filter. Target to background contrast ratio, Contrast and Peak Signal to Noise ratio are considered for performance evaluation of the enhancement algorithm. The mini-MIAS mammographic database was employed for testing the accuracy of the proposed method and the result was promising.

The proposed method focuses on the prediction of breast cancer in its early stage. It is very difficult to detect microcalcification due to its small size and low contrast with respect to the surrounding tissues. A new, fast and simple enhancement technique is considered for early detection of breast cancer by enhancing microcalcification features using morphology and LOG filter. Target to background contrast ratio, Contrast and Peak Signal to Noise ratio are considered for performance evaluation of the enhancement algorithm. The mini-MIAS mammographic database was employed for testing the accuracy of the proposed method and the result was promising.

This paper presents an approach to the gallbladder anomaly analysis using 3D shape modeling and 2D image segmentation. The 3D shape of gallbladder is represented as a surface mesh model, which is constructed from the contours segmented in CT data by a scheme of propagation based voxel classification. To better extract the shape feature, the surface mesh is further down-sampled by a decimation filter and smoothed by a Taubin algorithm. The curvatures are computed on the regularized mesh for the salience region localization on the surface. With a given tolerance the 3D shape can be decomposed and represented as 3D ellipsoids, which reveal the shape topology and anomaly of a gallbladder. Preliminary experiments on 10 sets of gallbladder CT data have shown the promising results in the shape structure extraction and shape anomaly detection.

Diseases that are characterized by the disordered growth of cells that, in many cases, have the property of invading tissues and organs are commonly called cancer. Such cells divide quickly and the invasion can be very aggressive and uncontrolled, resulting in formation of malignant tumors Mammographic images from libraries of the American digital database DDSM were used in this research for digital improvement and characteristic analysis using the OpenVino computer program This work has as main objective to analyze mammography images of breast nodules and to propose a method of classification by shape and texture using computer programs that can maximize the accuracy in the correct diagnosis regarding the malignancy or not of a tumor. It is a tool that it can be useful as a contribution in the interpretation of the results to mastologists who identify such nodules through the analyzed radiological images.

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This study compares the performance of Computer-Aided Detection (CAD) systems for mammogram analysis using two prominent machine learning techniques: These are the kind of models Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). The main goal is, therefore, to assess the performance of these models in correctly classifying abnormal mammogram images, especially of early-stage breast cancer. The work uses a dataset of mammogram images with labels, removing outliers and repeating rows and columns, then normalizing equals and providing input data for both the SVM and CNN models. The former chosen quantities were introduced as performance metrics known as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC) for both models. Research shows that the accuracy of both SVM and CNN is equivalent and that CNN has a higher sensitivity and specificity, indicating it could be more efficient in early cancer detection. The implications of these findings highlight the beneficial use of deep learning models in medical images, especially CNN models. This research is useful for the current development of CAD systems and gives potential future applications of AI in the context of diagnosis in clinics.

This paper presents an approach to the gallbladder anomaly analysis using 3D shape modeling and 2D image segmentation. The 3D shape of gallbladder is represented as a surface mesh model, which is constructed from the contours segmented in CT data by a scheme of propagation based voxel classification. To better extract the shape feature, the surface mesh is further down-sampled by a decimation filter and smoothed by a Taubin algorithm. The curvatures are computed on the regularized mesh for the salience region localization on the surface. With a given tolerance the 3D shape can be decomposed and represented as 3D ellipsoids, which reveal the shape topology and anomaly of a gallbladder. Preliminary experiments on 10 sets of gallbladder CT data have shown the promising results in the shape structure extraction and shape anomaly detection.

Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. Currently, no computerized method is available for fullyor semi-automated detection of CMBs. In this paper, we propose a CAD system for the detection of CMBs to speed up visual analysis in population-based studies. Our method consists of three steps: (i) skull-stripping (ii) initial candidate selection (iii) reduction of false-positives using a two layer classification and (iv) determining the anatomical location of CMBs. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The geometrical, intensity-based and local image descriptor features were used in the classification steps. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The sensitivity for CMB detection was 90% with, on average, 4 false-positives per subject.

In many cases, masses in X-ray mammograms are subtle and their detection can benefit from an automated system serving as a diagnostic aid. It is to this end that the authors propose in this paper, a new computer aided mass detection for breast cancer diagnosis. The first step focuses on wavelet filters enhancement which removes bright background due to dense breast tissues and some film artifacts while preserving features and patterns related to the masses. In the second step, enhanced image is computed by Entropy Maximization Thresholding (EMT) to obtain segmented masses. The efficiency of 98,181% is achieved by analyzing a database of 84 mammograms previously marked by radiologists and digitized at a pixel size of 343µmm × 343µ mm. The segmentation results, in terms of size of detected masses, give a relative error on mass area that is less than 8%. The performance of the proposed method has also been evaluated by means of the receiver operating-characteristics (ROC) analysis. This yielded respectively, an area (Az) of 0.9224 and 0.9295 under the ROC curve whether enhancement step is applied or not. Furthermore, we observe that the EMT yields excellent segmentation results compared to those found in literature.

Breast cancer screening rules cannot avoid the infection from happening, but they can offer assistance identify cancer early, when treatment comes about are most compelling. The survival rate of breast cancer patients depends on the woman's age and the organize of the disease at the time of determination. Various thinks about have affirmed that early discovery of breast cancer can spare a woman's life. Many years of observing of organized mammography have affirmed the levelheadedness of such breast cancer screening, particularly in ladies at tall chance of creating the illness. This chance is most elevated between the ages of 50 and 69, so organized screening of ladies is suggested at this age.

Breast cancer is one of the most common cancers among women worldwide. Mass detection from mammogram helps in early detection of breast cancer. A Computer Aided Detection (CAD) system which will help to identify and detect the malignant masses in human breast in an accurate and cost effective way is needed. It has been the dream and aim of researchers to have a CAD system with maximum sensitivity and low false positives per image (FPI). Detection of mass from human breast is difficult due to its abundant morphological characteristics and ambiguous margins. Mass detection performance can be improved by using effective preprocessing mechanism and morphological characteristics of the mass regions. As a mass develops, it disturbs the breast parenchyma and spreads by developing multiple concentric layers. Morphological analysis of these concentric layers is a corner stone in mass detection algorithm. Various CAD systems using concentric morphology model exist in the literature. In this paper an attempt has been made to summarize some of the existing CAD systems which use concentric morphology model for early and accurate detection of masses.

Background: Single reading with computer aided detection (CAD) is an alternative to double reading for detecting cancer in screening mammograms. The aim of this study is to investigate whether the use of a single reader with CAD is more cost-effective than double reading. Methods: Based on data from the CADET II study, the cost-effectiveness of single reading with CAD versus double reading was measured in terms of cost per cancer detected. Cost (Pound (£), year 2007/08) of single reading with CAD versus double reading was estimated assuming a health and social service perspective and a 7 year time horizon. As the equipment cost varies according to the unit size a separate analysis was conducted for high, average and low volume screening units. One-way sensitivity analyses were performed by varying the reading time, equipment and assessment cost, recall rate and reader qualification. Results: CAD is cost increasing for all sizes of screening unit. The introduction of CAD is cost-increasing compared to double reading because the cost of CAD equipment, staff training and the higher assessment cost associated with CAD are greater than the saving in reading costs. The introduction of single reading with CAD, in place of double reading, would produce an additional cost of £227 and £253 per 1,000 women screened in high and average volume units respectively. In low volume screening units, the high cost of purchasing the equipment will results in an additional cost of £590 per 1,000 women screened. One-way sensitivity analysis showed that the factors having the greatest effect on the cost-effectiveness of CAD with single reading compared with double reading were the reading time and the reader's professional qualification (radiologist versus advanced practitioner). Conclusions: Without improvements in CAD effectiveness (e.g. a decrease in the recall rate) CAD is unlikely to be a cost effective alternative to double reading for mammography screening in UK. This study provides updated estimates of CAD costs in a full-field digital system and assessment cost for women who are re-called after initial screening. However, the model is highly sensitive to various parameters e.g. reading time, reader qualification, and equipment cost.

Rationale and Objectives-The ability to automatically detect and monitor implanted devices may serve an important role in patient care by aiding the evaluation of device and treatment efficacy. The purpose of this research was to develop a system for the automated detection of one-way endobronchial valves that were implanted for less-invasive lung volume reduction. Materials and Methods-Volumetric thin-section CT data was obtained for 194 subjects; 95 subjects implanted with 246 devices were used for system development and 99 subjects implanted with 354 devices were reserved for testing. The detection process consisted of pre-processing, pattern-recognition based detection, and a final device selection. Following the pre-processing, a set of classifiers were trained using AdaBoost to discriminate true devices from false positives. The classifiers in the cascade used two simple features (either the mean or maximum attenuation) of a local region computed at multiple fixed landmarks relative to a template model of the valve. Results-FROC analysis was performed for the evaluation; the system could be set so the mean sensitivity was 96.5% with a mean of 0.18 false positives per subject. If knowledge of the number of implanted devices were incorporated, the sensitivity would be 96.9% with a mean of 0.061 false positives per subject; this corresponds to a total of 12 false negatives and 6 false positives for the 99 subjects in the test dataset.

Masses are one of the common signs of nonpalpable breast cancer visible in mammograms. However, due to its irregular and obscured margin, variability in size, and occlusion within dense breast tissue, a mass may be missed during screening. In this paper, we propose a novel approach for automatic detection of mammographic masses using an iterative method of multilevel high-to-low intensity thresholding, followed by region growing and reduction of false positives, in which an image is considered as a 3D topographic map with intensity as the third dimension. At each iteration, first, the focal regions of masses are obtained by thresholding, and then potential sites of masses are extracted from the focal regions with a newly developed region growing technique. Finally, false positives are reduced using contrast and distance between two potential mass regions, and by using a classifier after the extraction of shape-and orientation-based features. The performance of the method is evaluated with 120 scanned-film images, including 55 images with 57 masses and 65 normal images from the mini-MIAS database; 555 scanned-film images, including 355 images with 370 masses and 200 normal images from the DDSM; and 219 digital radiography (DR) images, including 99 images with 120 masses and 120 normal images from a local database. For the mini-MIAS, DDSM, and DR images 90% sensitivity is achieved at a rate of 4.4, 0.99, and 1.0 false positive per images, respectively.

The implementation of a database of digitised mammograms is discussed. The digitised images were collected beginning in 1999 by a community of physicists in collaboration with radiologists in several Italian hospitals as a first step in developing and implementing a computer-aided detection (CAD) system. All 3,369 mammograms were collected from 967 patients and classified according to lesion type and morphology, breast tissue and pathology type. A dedicated graphical user interface was developed to visualise and process mammograms to support the medical diagnosis directly on a high-resolution screen. The database has been the starting Riassunto In questo lavoro viene discussa l'implementazione di un database immagini mammografiche digitalizzate. Le immagini sono state raccolte dal 1999 da un gruppo di fisici in collaborazione con radiology di alcuni ospedali italiani, come primo passo dello sviluppo e implementazione di un sistema di Computer Aided Detection (CAD). I 3369 mammogrammi appartengono a 967 pazienti e sono classificati secondo I tipi e la morfologia delle lesioni, il tessuto mammario e i tipi di patologie. Una interfaccia grafica opportunamente progettata è stata sviluppata per la visualizzazione e l'elaborazione delle mammografie digitalizzate al fine di BREAST RADIOLOGY SENOLOGIA

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection.

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical Computed Tomography (CT) images with 1.25 mm slice thickness is presented. The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural classifier for false-positive finding reduction, are described. The results obtained on the collected database of lung CT scans are discussed.

In this article we present a classification system for an automatic detection of masses in digitized mammographic images. The systems consists in three main processing levels: a) image segmentation for the localization of regions of interest (ROIs); b) ROI ...

The area under the ROC curve was found to be A z = 0.783± 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

A computer-aided detection ͑CAD͒ system for the selection of lung nodules in computer tomography ͑CT͒ images is presented. The system is based on region growing ͑RG͒ algorithms and a new active contour model ͑ACM͒, implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: ͑1͒ the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; ͑2͒ a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; ͑3͒ a double-threshold cut and a neural network are applied to reduce the false positives ͑FPs͒. After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans ͑about 4700 sectional images͒ with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (Computed Tomography) images.

In this paper medical applications on a Grid infrastructure, the MAGIC-5 Project, are presented and discussed. MAGIC-5 aims at developing Computer Aided Detection (CADe) software for the analysis of medical images on distributed databases by means of GRID Services. The use of automated systems for analyzing medical images improves radiologists' performance; in addition, it could be of paramount importance in screening programs, due to the huge amount of data to check and the cost of related manpower. The need for acquiring and analyzing data stored in different locations requires the use of Grid Services for the management of distributed computing resources and data. Grid technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays presently associated with the diagnosis in the screening programs. The MAGIC-5 project develops algorithms for the analysis of mammographies for breast cancer detection, Computed-Tomography (CT) images for lung cancer detection and Positron Emission Tomography (PET) images for the early diagnosis of Alzheimer Disease (AD). A Virtual Organization (VO) has been deployed, so that authorized users can share data and resources and implement the following use cases: screening, tele-training and tele-diagnosis for mammograms and lung CT scans, statistical diagnosis by comparison of candidates to a distributed data-set of negative PET scans for the diagnosis of the AD. A small-scale prototype of the required Grid functionality was already implemented for the analysis of digitized mammograms.

A study was performed to determine the accuracies and reproducibilities of the CT numbers of simulated lung nodules imaged with multi-detector CT scanners. The nodules were simulated by spherical balls of three diameters ͑4.8, 9.5, and 16 mm͒ and two compositions ͑50 and 100 mg/ cc CaCO 3 in water-equivalent plastic͒. All were scanned in a liquid-water-filled container at the center of a water-equivalent-plastic phantom and in air cavities within the same phantom using GE multidetector CT scanners. The nodules were also scanned within simulated lung regions in an anthropomorphic thorax section phantom that was bolused on both sides with water-equivalent slabs. Results were compared for three scanning protocols-the protocol for the National Lung Screening Trial ͑NLST͒, the protocol for the Lung Tissue Research Consortium ͑LTRC͒ study, and a high resolution ͑small pitch, thin slice and small scan interval͒ higher dose "gold standard" protocol. Scans were repeated three times with each protocol to assess reproducibility. The CT numbers of the nodules in water were found to be nearly independent of nodule size. However, the presence and the size of an air cavity surrounding a nodule had a significant effect ͑e.g., the CT number of a 50 mg/ cc nodule was 64 HU in water, 37 HU in a 1.8 cm diameter air cavity, and 19 HU in a 4.4 cm diameter air cavity͒. This variability of CT number with size of air cavity may affect the results of the LTRC study in which patients are scanned at both full inspiration and full expiration. The CT numbers of the 9.5 and 16 mm diameter nodules within the anthropomorphic phantom were highly reproducible ͑average standard deviations of 2 HU or less͒ for all protocols. On the other hand, both accuracy and reproducibility were significantly degraded for the 4.8 mm diameter nodules, especially for the NLST ͑2.5 mm thickness, 2 mm slice interval͒ technique. Use of thinner slice ͑1.25 mm͒ and slice interval ͑1.25 mm͒ scans that can be reconstructed retrospectively from the multi-detector helical CT projection data of the standard NLST protocol yield CT numbers for the 4.8 mm diameter nodules that are more accurate and reproducible than those of the standard NLST technique. In general, the CT numbers of the nodules were found to be lower at positions near the centers of the lungs and near the spine, which is probably due to increased beam hardening in those regions. Also, larger nodules were found to have higher CT numbers than smaller nodules, consistent with results obtained on early single slice GE CT scanners. Until manufacturers develop quantitative CT scanners with improved x-ray beam hardening and scatter corrections, it is recommended that reference phantoms be employed to more accurately assess the calcium contents of patient lung nodules in screening and tissue characterization studies and in eventual computer-aided detection and diagnosis applications.

Given a segmented CT scan data of the colon represented as a triangle mesh, our water-plane algorithm will detect polyp candidates. The water-plane method comprises of pouring water into a polyp protrusion from the outside of the colon and in raising the "water-plane" until it cannot be incremented any further without causing water leakage. The method starts at a vertex and uses average normal of all triangles adjacent to the starting vertex to generate the initial water-plane, which will make the starting vertex "wet" but leave its neighboring vertices "dry". The method will continue to wet neighboring vertices one by one and then their neighbors and so on until the water-plane cannot move any further without causing water leakage. The water-plane movement alternates between just raising the water level in completely convex regions and tilting about one or two anchor vertices that have neighbors that would get wet if the water level was raised any more. The final set of wet vertices is a cluster that is an initial polyp candidate. The water-plane method was compared against the current polyp candidate detection method in our Computer Aided Detection of Colon Polyps software pipeline, called the surface curvature method. It finds clusters of connected vertices that all exhibit elliptical curvature. The water-plane method showed multiple improvements in polyp candidate detection. It detected polyp candidates missed by the surface curvature method. It exhibited continuous polyp candidate regions instead of nonuniform or incomplete regions detected by the surface curvature method. And finally, it avoided some false positive detections reported by surface curvature method.

Size is an important metric for pulmonary nodule characterization. Furthermore, it is an important parameter in measuring the performance of computer aided detection systems since they are always qualified with respect to a given size range of nodules. The first 120 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation were used in this study. For documentation, each inspected lesion was reviewed independently by four expert radiologists and, when a lesion was considered to be a nodule larger than 3 mm, the radiologist provided boundary markings in each image in which the nodule was contained. Three size metrics were considered: a uni-dimensional and a bi-dimensional measure on a single image slice and a volumetric measurement based on all the image slices. In this study we analyzed the boundary markings of these nodules in the context of these three size metrics to characterize the inter-radiologist variation and to examine the difference between these metrics. A data set of 63 nodules each having four observations was analyzed for inter-observer variation and an extended set of 252 nodules each having at least one observation was analyzed for the difference between the metrics. A very high inter-observer variation was observed for all these metrics and also a very large difference among the metrics was observed.

To evaluate the number of actual detections versus "accidental" detections by a computer-aided detection (CAD) system for small nodular lung cancers (≤30 mm) on chest radiographs, using two different criteria for measuring performance. A Food-and-Drug-Administrationapproved CAD program (version 1.0; Riverain Medical) was applied to 34 chest radiographs with a "radiologistmissed" nodular cancer and 36 radiographs with a radiologist-mentioned nodule (a newer version 3.0 was also applied to the 36-case database). The marks applied by this CAD system consisted of 5-cm-diameter circles. A strict "nodule-in-center" criterion and a generous "nodulein-circle" criterion were compared as methods for the calculation of CAD sensitivity. The increased sensitivities by the nodule-in-circle criterion were considered as nodules detected by chance. The number of false-positive (FP) marks was also analyzed. For the 34 radiologist-missed cancers, the nodule-in-circle criterion caused eight more cancers (24%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results. For the 36 radiologist-mentioned nodules, the nodule-in-circle criterion caused seven more lesions (19%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results, and three more lesions (8%) to be detected by chance when using the version 3.0 results. Version 1.0 yielded a mean of six FP marks per image, while version 3.0 yielded only three FP marks per image. The specific criteria used to define true-and false-positive CAD detections can substantially influence the apparent accuracy of a CAD system.

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8×8, 12×12 and 20×20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8×8 cell template. For the 12×12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20×20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient.

Computer-aided detection (CAD) has shown potential to assist physicians in the detection of lung nodules on chest radiographs, but widespread acceptance has been stymied by high false-positive rates. Few studies have examined the potential for dual energy subtraction (DES) to improve CAD performance. Materials and Methods: Institutional review board approval was obtained, the requirement for informed consent was waived because the study was retrospective, and practices conformed to Health Insurance Portability and Accountability Act regulations. The CAD program was applied retrospectively to dual energy posteroanterior (PA) chest radiographs of 36 patients (17 women, 19 men, mean age 69 y) with 48 pathology proven lung nodules. Results were analyzed to determine the stand-alone CAD program false-positive rates, and sensitivity by nodule subtlety and location. Statistical analysis was performed using the w 2 or Fisher exact tests for independence of sensitivities between standard PA and DES radiography. Differences in the mean false-positives per image (FPPI) between radiographic modalities were determined using the paired Students t test, and bootstrap confidence intervals were obtained to confirm results. Results: The sensitivity of the CAD program with the standard PA was 46% (22 of 48 nodules) compared with 67% (32 of 48 nodules) using the DES soft tissue or bone-subtracted view (P = 0.064). The average number of FPPI identified by CAD was significantly lower using DES (FPPI soft tissue = 1.64) when compared with the standard PA chest radiograph (FPPI PA = 2.39) (P<0.01). Conclusions: DES has the potential to improve stand-alone CAD performance by both increasing sensitivity for certain subtle lung cancer lesions and decreasing overall CAD false-positive rates.

As long as breast cancer remains the leading cause of cancer deaths among female population world wide, developing tools to assist radiologists during the diagnosis process is necessary. However, most of the technologies developed in the imaging laboratories are rarely integrated in this assessing process, as they are based on information cues differing from those used by clinicians. In order to grant Computer Aided Diagnosis (CAD) systems with these information cues when performing non-aided diagnosis, better segmentation strategies are needed to automatically produce accurate delineations of the breast structures. This paper proposes a highly modular and flexible framework for segmenting breast tissues and lesions present in Breast Ultra-Sound (BUS) images. This framework relies on an optimization strategy and high-level de-scriptors designed analogously to the visual cues used by radiologists. The methodology is comprehensively compared to other sixteen published methodologies de...

Immersive Colonography allows medical professionals to navigate inside the intricate tubular geometries of subject-specific 3D colon images using Virtual Reality displays. Typically, camera travel is performed via Fly-Through or FlyOver techniques that enable semi-automatic traveling through a constrained, well-defined path at user controlled speeds. However, Fly-Through is known to limit the visibility of lesions located behind or inside haustral folds, while FlyOver requires splitting the entire colon visualization into two specific halves. In this paper, we study the effect of immersive Fly-Through and FlyOver techniques on lesion detection, and introduce a camera travel technique that maintains a fixed camera orientation throughout the entire medial axis path. While these techniques have been studied in non-VR desktop environments, their performance is yet not well understood in VR setups. We performed a comparative study to ascertain which camera travel technique is more appropriate for constrained path navigation in Immersive Colonography. To this end, we asked 18 participants to navigate inside a 3D colon to find specific marks. Our results suggest that the FlyOver technique may lead to enhanced lesion detection at the cost of higher task completion times, while the Fly-Through method may offer a more balanced trade-off between both speed and effectiveness, whereas the fixed camera orientation technique provided seemingly inferior performance results. Our

The Channeler Ant Model (CAM) is an algorithm based on virtual ant colonies, conceived for the segmentation of complex structures with different shapes and intensity in a 3D environment. It exploits the natural capabilities of virtual ant colonies to modify the environment and communicate with each other by pheromone deposition. When applied to lung CTs, the CAM can be turned into a Computer Aided Detection (CAD) method for the identification of pulmonary nodules and the support to radiologists in the identification of early-stage pathological objects. The CAM has been validated with the segmentation of 3D artificial objects and it has already been successfully applied to the lung nodules detection in Computed Tomography images within the ANODE09 challenge. The model improvements for the segmentation of nodules attached to the pleura and to the vessel tree are discussed, as well as a method to enhance the detection of low-intensity nodules. The results on five datasets annotated with different criteria show that the analytical modules (i.e. up to the filtering stage) provide a sensitivity in the 80 − 90% range with a number of FP/scan of the order of 20. The classification module, although not yet optimised, keeps the sensitivity in the 70 − 85% range at about 10 FP/scan, in spite of the fact that the annotation criteria for the training and the validation samples are different.

Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions.

Summary Breast cancer continues to be a significant public health problem among women around the world. It has become the number one cause of cancer deaths amongst Malaysian women. The key to improve the breast cancer prognosis is by early detection. The important sign for the breast cancer detection is the presence of lesion such as microcalcification clusters (MCCs). In

In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods. Index Terms-Computed tomographic colonography (CTC), multiple-instance learning, semidefinite programming, video analysis. I. INTRODUCTION C OLON cancer is the second leading cause of cancer-related death in the United States [1]. Computed tomographic colonography (CTC), also known as virtual colonoscopy when a fly-through viewing mode is used, provides a less invasive alternative to optical colonoscopy Manuscript

Copyright © 2011 Artit C. Jirapatnakul et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfull...

A computer-aided detection ͑CAD͒ system for the selection of lung nodules in computer tomography ͑CT͒ images is presented. The system is based on region growing ͑RG͒ algorithms and a new active contour model ͑ACM͒, implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: ͑1͒ the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; ͑2͒ a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; ͑3͒ a double-threshold cut and a neural network are applied to reduce the false positives ͑FPs͒. After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans ͑about 4700 sectional images͒ with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) the training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features extracted from co-occurrence matrix containing spatial statistics information on ROI pixel grey tones. A dissimilarity representation of these features is made before the classification. A feed-forward neural network is employed to distinguish pathological records, from nonpathological ones by the new features. The results obtained in terms of sensitivity and specificity will be presented.

Background Computer-aided detection (CAD) has been shown to increase the sensitivity for detection of pulmonary nodules in adults. This study reports initial findings utilizing a CAD system for the detection of pediatric pulmonary nodules. Objective To assess the performance of CAD and pediatric radiologists in the detection of pediatric pulmonary nodules. Materials and methods CT scans from a series of pediatric patients with known primary tumors and lung nodules were analyzed by four radiologists and a commercially available CAD system. IRB approval was obtained. Sensitivities were calculated for detection according to nodule size and location. Results In 24 children (age 3-18 years) 173 nodules were identified. Overall the sensitivity of CAD was 34%, but the sensitivity of CAD for detection of nodules 4.0 mm or larger was 80%. Overall radiologist sensitivity ranged from 68% to 79%. There were 0.9 CAD false-positives and 0.3-2.4 radiologist false-positives per study. Conclusion CAD in our pediatric oncology patients had good sensitivity for detection of lung nodules 4 mm and larger with a low number of false-positives. However, the sensitivity was considerably less for nodules smaller than 4 mm.

The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing front-end workstation and b) the ability to service real-world clinician queries across a distributed and federated database. The MammoGrid project will prove the viability of the Grid by harnessing its power to enable radiologists from geographically dispersed hospitals to share standardized mammograms, to compare diagnoses (with and without computer aided detection of tumours) and to perform sophisticated epidemiological studies across national boundaries. This paper outlines the approach taken in MammoGrid to seamlessly connect radiologist workstations across a Grid using an "information infrastructure" and a DICOMcompliant object model residing in multiple distributed data stores in Italy and the UK.

The purpose of this study was to compare sensitivity for detection of pulmonary nodules in MDCT scans and reading time of radiologists when using CAD as the second reader (SR) respectively concurrent reader (CR). Four radiologists analyzed 50 chest MDCT scans chosen from clinical routine two times and marked all detected pulmonary nodules: first with CAD as CR (display of CAD results immediately in the reading session) and later (median 14 weeks) with CAD as SR (display of CAD markers after completion of first reading without CAD). A Siemens LungCAD prototype was used. Sensitivities for detection of nodules and reading times were recorded. Sensitivity of reading with CAD as SR was significantly higher than reading without CAD (p< 0.001) and CAD as CR (p<0.001). For nodule size of 1.75 mm or above no significant sensitivity difference between CAD as CR and reading without CAD was observed; e.g., for nodules above 4 mm sensitivity was 68% without CAD, 68% with CAD as CR (p=0.45) and 75% with CAD as SR (p<0.001). Reading time was significantly shorter for CR (274 s) compared to reading without CAD (294 s; p=0.04) and SR (337 s; p< 0.001). In our study CAD could either speed up reading of chest CT cases for pulmonary nodules without relevant loss of sensitivity when used as CR, or it increased sensitivity at the cost of longer reading times when used as SR.