Breast cancer screening using computational approaches

Breast cancer is the primary and the most common disease found among women. Due to this the mortality rate has increased a lot as compare to other cancers found in women. Mammography is found to be most powerfuldetection technique which is been helpful to detect earlier cancerous tumor and increase success rate of treatment. Classification is a machine learning procedure thatclassify the data into predefined class labels which are used to predict the data according to those Classes.With this objective, a survey on mammogram classification is done by considering various feature extraction and classification methods used for classification of mammogram images. We compare the results of classification obtained from various feature extraction and classification techniques.

2012

The presence of microcalcification clusters in the mammogram image is a significant sign for the breast cancer at an early stage. The early detection increases the chance for successful treatment and complete recovery of the patient [1]. At present, the detection of microcalcification is still difficult because of their fuzzy nature, low contrast and low distinguish-ability from their surroundings [2, 3]. The interpretations of their presence are very difficult because of their morphological features. Microcalcifications are very small, typically between 0.1 and 1.0 mm, which means that they can be easily overlooked by a radiologist [4]. The purpose of this paper is to identify the location of suspicious areas to assist radiologists for diagnosis. The proposed method is divided into four steps: (a) image preprocessing (b) image enhancement using image morphology (c) individual calcification detection using intensity threshold, where pixels with high intensity are considered as suspi...

Lecture Notes in Computer Science, 2012

In this paper, we present an evaluation of four different algorithms, based on Mathematical Morphology, to detect the occurrence of microcalcifications in digital mammogram images from the mini-MIAS database. Results provided by TMVA produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. ROC area measures the performance of automatic classification, which produced its highest value 0.976 for Gaussian kernel, followed by polynomial kernel, which produced 0.972. An additional parameter, called Signal Efficiency*Purity (SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise.

2010 IEEE International Conference on Image Processing, 2010

We present in this paper a supervised approach for automatic detection of micro-calcifications. The system is based on learning the different morphology of the micro-calcifications using local features, which are extracted using a bank of filters. Afterwards, this set of features is used to train a pixelbased boosting classifier which at each round automatically selects the most salient one. Therefore, when a new mammogram is tested only the salient features are computed and used to classify each pixel of the mammogram as being part of a micro-calcification or actually being normal tissue. The experimental results shows the validity of our approach. Moreover, the robustness of our method is also demonstrated using a digitised database for the learning process and a different one for the testing, providing satisfactory results.

Breast tumors, mainly in women, can represent the presence of malignity. Through biopsy Le Gal has proven that the format of tumor related micro-calcifications indicates the probability of presence of carcinoma. Rugose shaped micro-calcifications are present in 66% of malignant lesions and in 23% of non-malignant ones. The presence of very dense agglomerates of small micro-calcifications generally indicates the presence of malignant tumors. We developed and implemented algorithms to both analyze mammographic images and detect micro-calcifications having rugose boundaries. This paper presents a tool to assist radiologists in breast cancer diagnosis.

Ain Shams Engineering Journal, 2019

Computer-aided diagnosis (CAD) approach is presented as strong frameworks to solve the inaccuracy problems. The major purpose of this paper is to improve a CAD system depended on supervised classification that can be useful in diagnosing and detecting the changes of breast cancers in digitized mammograms earlier, accurately and faster than standard examination programs by applying CAD according to image processing techniques beginning with preprocessing step, segmentation, feature extraction and finally classification stage. The work presented in this study is based on the integration of different features such as shape, texture and invariant moment features. This integration achieved best results for sensitivity and specificity rather than using the one type of features in breast cancer classification. The accuracy of our integration system reached 96% in the automatic mode of ANN while best accuracy accomplished by features result according to invariant moments that reached 97% by ANN in an automatic way.

IEEE Transactions on Medical Imaging, 1993

A systematic method for the detection and segmentation of microcalcifications in mammograms is presented. It is important to preserve size and shape of the individual calcifications as exactly as possible. A reliable diagnosis requires both rates of false positives as well as false negatives to be extremely low. The proposed approach uses a two-stage algorithm for spot detection and shape extraction. The first stage applies a weighted difference of Gaussians filter for the noise-invariant and sizespecific detection of spots. A morphological filter reproduces the shape of the spots. The results of both filters are combined with a conditional thickening operation. The topology and the number of the spots are determined with the first filter, and the shape by means of the second. The algorithm is tested with a series of real mammograms, using identical parameter values for all images. The results are compared with the judgement of radiological experts, and they are very encouraging. The described approach opens up the possibility of a reproducible segmentation of microcalcifications, which is a necessary precondition for an efficient screening program.

2012

Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branch standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.

Computación y Sistemas

Breast cancer is the most common cause of death in women and the second leading cause of cancer deaths worldwide. Primary prevention in the early stages of the disease becomes complex as the causes remain almost unknown. However, some typical signatures of this disease, such as masses and microcalcifications appearing on mammograms, can be used to improve early diagnostic techniques, which is critical for women's quality of life. X-ray mammography is the main test used for screening and early diagnosis, and its analysis and processing are the keys to improving breast cancer prognosis. In this work, an effective methodology to detect microcalcifications in digitized mammograms is presented. This methodology is based on the synergy of image processing, pattern recognition and artificial intelligence. The methodology consists in four stages: image selection, image enhancement and feature extraction based on mathematical morphology operations applying coordinate logic filters, image segmentation based on partitional clustering methods such as k-means and self organizing maps and finally a classifier such as an artificial metaplasticity multilayer perceptron. The proposed system constitutes a promising approach for the detection of Microcalcifications. The experimental results show that the proposed methodology can locate Microcalcifications in an efficient way. The best values obtained in the experimental results are: accuracy 99.93% and specificity 99.95%, These results are very competitive with those reported in the state of the art.

2012

Breast cancer remains one of the deadliest diseases among women. Microcalcifications can be an early indicator of a breast cancer. Thus when they are present their detection during a screening test is very crucial. But due to their small size and low contrast in mammographies their detection is difficult. Therefore many computer aided diagnosis mathods have been developped to help and assist rediologist during their screening tests. This paper presents a novel approach to detect microcalcifications on digitized mammaographies using fuzzy logic and support vector machines with 9 statistical and geometric features. Our method was tested on 120 cases from the Mias database including 22 microcalcification cases, 60 normal cases and 70 abnormal cases.We have reached an Az Value of 0,91 making this method very comparable to the State Of Art works.