Papers by Rekha Lakshmanan
Bonfring international journal of networking technologies and applications, Dec 30, 2012
The proposed method focuses on the prediction of breast cancer in its early stage. It is very dif... more 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.
Enhancement of microcalcification features in mammograms using zero-crossings of the Contourlet transform
Journal of Emerging Trends in Engineering and Applied Sciences, 2011
Computer aided techniques can assists radiologists in detecting microcalcification, a crucial evi... more Computer aided techniques can assists radiologists in detecting microcalcification, a crucial evidence in mammogram for the early diagnosis of breast cancer. A new approach is proposed in this work for early detection of breast cancer by enhancing microcalcification features using selected zero-crossings of Contourlet transform to enhance edges and to reduce image noise. Parent-child-cousin relationship among the contourlet coefficients at various levels was employed to retain the strong edge information that corresponds to the relevant features and to minimize the artifacts. Sensitivity, Specificity, contrast, Target to Background contrast ratio, Contrast Improvement Index, Average Signal to Noise Ratio and Peak Signal to Noise Ratio parameters are considered for performance evaluation of the enhancement algorithm. The mini-MIAS mammographic image database was employed for testing the accuracy of proposed method and the results were promising.
Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach
Journal of Intelligent & Fuzzy Systems, 2020
Ijca Proceedings on Emerging Technology Trends on Advanced Engineering Research 2012, Dec 12, 2012
The proposed method focuses on the prediction of breast cancer in its early stage. It is very dif... more 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.
Investigation of Modulus Maxima of Contourlet Transforms for Enhancing Mammographic Features
Communications in Computer and Information Science, 2013
Enhancing features of mammographic image assists radiologists in the early detection of breast ca... more Enhancing features of mammographic image assists radiologists in the early detection of breast cancer. In this paper, an enhancement technique using selected modulus maxima of the Contourlet transform is employed to enhance the microcalcification features in mammographic image while simultaneously reducing image noise. Strong edge information corresponding to relevant features was retained based on a parent child relationship among contourlet coefficients at various levels. Simulations were carried out to examine the utility of the proposed technique in mammographic image enhancement. The mini – MIAS database was employed to test the accuracy of proposed method. Contrast improvement index, Peak Signal to Noise Ratio, Target to Background Contrast ratio and Tenengrad Criterion were considered for a evaluating the performance of the proposed methods.
Bonfring International Journal of Networking Technologies and Applications, 2012
The proposed method presents a new classification approach to microcalcification detection in mam... more The proposed method presents a new classification approach to microcalcification detection in mammograms using morphology, Contourlet Transform and Artificial Neural Network. Early detection of breast cancer is possible by enhancing microcalcification features obtained using morphology and singularities of Contourlet Transform. The significant edge information indicating the relevant features in various decomposition levels are preserved while removing the artifacts. These features are utilized to detect microcalcifications by classification employing the Back Propagation Neural Network. Target to background contrast ratio, Contrast and Peak Signal to Noise ratio are considered for performance evaluation of the enhancement algorithm. The accuracy of the classification algorithm is 95%. The mini-MIAS mammographic database is employed for testing the accuracy of the proposed method and the results are promising.
Pectoral Muscle Boundary detection - A preprocessing method for early breast cancer detection
2014 World Automation Congress (WAC), 2014
Pectoral Muscle (PM), a significant region in Medio-Lateral Oblique (MLO) view of mammogram may a... more Pectoral Muscle (PM), a significant region in Medio-Lateral Oblique (MLO) view of mammogram may adversely affect anomaly detection due to its resemblance to abnormal tissues. The removal of PM region can be considered as a prerequisite step for early breast cancer detection using mammographic images. The principal component of PM boundary component is extracted using the orientation and eccentricity property of Canny edge detected components of coarse mammographic image obtained after a multiscale decomposition technique using Laplacian Pyramid (LP). The principal component of PM boundary is extended to top and left boundaries using nearest neighbor approach. The algorithm was tested on images from the Mammographic Image Analysis Society (MIAS) database as well as mammograms obtained from a representative set of Indian populace provided by Lakeshore Hospital Kochi, India. On comparison with the PM boundary assessed by radiologists, the proposed method yielded an average false positive rate of 0.28%, average false negative rate of 3.67% and low Hausdorff distance for 83 images in mammographic database. Based on the performance analysis of the proposed algorithm, it is observed that 97% of images have an average error less than 3 mm which is promising.
Straight Line Approximation of Pectoral Muscle in Mammogram Using the Method of Optimum Thresholding
2014 Fourth International Conference on Advances in Computing and Communications, 2014
In Medio-Lateral Oblique (MLO) view of mammogram, the presence of pectoral muscle may sometimes a... more In Medio-Lateral Oblique (MLO) view of mammogram, the presence of pectoral muscle may sometimes affect the detection of architectural distortion due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. The straight line approximation of pectoral muscle is also very important in obtaining the breast tissue architecture, which helps to detect the presence of architectural distortion. In this paper, a novel approach for the straight line approximation of pectoral muscle using optimum thresholding is proposed. The process first selects an optimum threshold based on average gray level and Otsu's threshold. The selected region of interest (ROI) of the image is thresholded based on the optimum threshold and approximates the pectoral muscle boundary as a straight line. A set of images was selected for testing and the method is found to have an accuracy of 86.67%.
Enhancement of Microcalcification Features Using Morphology and Contourlet Transform
2012 International Conference on Advances in Computing and Communications, 2012
The proposed method utilizes morphology and contourlet transform for early detection of breast ca... more The proposed method utilizes morphology and contourlet transform for early detection of breast cancer by enhancing micro calcification features. This CAD technique helps the radiologists in reaching a better assessment. The significant edge information indicating the relevant features in various decomposition levels were preserved while removing the artifacts. 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 results were promising.
Automatic contrast enhancement using Selective Grey-Level Grouping
International Journal of Signal and Imaging Systems Engineering, 2010
Page 1. 126 Int. J. Signal and Imaging Systems Engineering, Vol. 3, No. 2, 2010 Copyright © 2010 ... more Page 1. 126 Int. J. Signal and Imaging Systems Engineering, Vol. 3, No. 2, 2010 Copyright © 2010 Inderscience Enterprises Ltd. Automatic contrast enhancement using Selective Grey-Level Grouping Rekha Lakshmanan KMEA ...
International Journal of Intelligent Systems Technologies and Applications, 2011
In this paper, we have performed a comparative analysis of various conventional contrast enhancem... more In this paper, we have performed a comparative analysis of various conventional contrast enhancement techniques (histogram equalisation and adaptive histogram equalisation), the recent fast grey-level grouping method (Chen et al., 2006a,b), the fuzzy logic method ) and a modified fuzzy logic method (Nair et al., in press) to find out which of these is well suited for automatic contrast enhancement for satellite images of the ocean, obtained from a variety of sensors. The principle of transforming the skewed histogram of the original image into a uniform histogram is used as the basis for all techniques. The performance of the different contrast enhancement algorithms is evaluated based on the visual quality and the Tenengrad criterion. The inter-comparison of different techniques was carried out on a standard low contrast image and also on different satellite images with different characteristics. Based on our study, we conclude that the modified fuzzy logic (Nair et al., in press) is well suited for automatic contrast enhancement of satellite images of the ocean.
Journal of Image and Graphics, 2015
Amethod for the detection of the most commonly missed breast cancer anomaly, Architectural distor... more Amethod for the detection of the most commonly missed breast cancer anomaly, Architectural distortion, is proposed here. The distorted abnormal structures associated with Architectural distortion in suspicious regions are extracted using geometrical properties of edge features based on an energy model. Contours obtained from a modified Single Univalue Segment Assimilating Nucleus filtered mammogram, are employed for this purpose. A Pectoral muscle delineation technique is incorporated in the proposed method to reduce false positive rate.A ranking value of these potential regions based on linear and converging properties is computed to identify the probable origins of architectural distortion. Experimental analysis is performed on 100 images obtained from Lakeshore Hospital, India. The results are verified by expert radiologists. The proposed algorithm is successful in 94 mammograms and the results are found to be promising.
SPIE Proceedings, 2004
The proposed method detects the most commonly missed breast cancer symptom, Architectural Distort... more The proposed method detects the most commonly missed breast cancer symptom, Architectural Distortion. The basis of the method lies in the analysis of geometrical properties of abnormal patterns that correspond to Architectural Distortion in mammograms. Pre-processing methods are employed for the elimination of Pectoral Muscle (PM) region from the mammogram and to localize possible centers of Architectural Distortion. Regions that are candidates to contain centroids of Architectural Distortion are identified using a modification of the isotropic SUSAN filter. Edge features are computed in these regions using Phase Congruency, which are thinned using Gradient Magnitude Maximization. From these thinned edges, relevant edge structures are retained based on three geometric properties namely eccentricity to retain near linear structures, perpendicular distance from each such structure to the centroid of the edges and quadrant support membership of these edge structures. Features for classification are generated from these three properties; a feed-forward neural network, trained using a combination of backpropagation and a metaheuristic algorithm based on Cuckoo search, is employed for classifying the suspicious regions identified by the modified filter for Architectural Distortion, as normal or malignant. Experimental analyses were carried out on mammograms obtained from the standard databases MIAS and DDSM as well as on images obtained from Lakeshore Hospital in Kochi, India. The classification step yielded a sensitivity of 89%, 89.8.7% and 97.6% and specificity of 90.9, 85 and 96.7% on 60 images from MIAS, 100 images from DDSM database and 100 images from Lakeshore Hospital respectively
Automatic contrast enhancement for low contrast images: a comparison of recent histogram based techniques
Page 1. Automatic Contrast Enhancement for Low Contrast Images: A Comparison of Recent Histogram ... more Page 1. Automatic Contrast Enhancement for Low Contrast Images: A Comparison of Recent Histogram Based Techniques Rekha Lakshmanan 1 , Madhu S. Nair 2 , M. Wilscy 3 and Rao Tatavarti 4 1 KMEA Engineering College, Aluva, Kerala, India rekhavibin@gmail.com ...
Signal, Image and Video Processing, 2011
In this paper, we evaluate the conventional contrast enhancement techniques [histogram equalizati... more In this paper, we evaluate the conventional contrast enhancement techniques [histogram equalization (HE), adaptive HE] and the recent gray-level grouping method and the fuzzy logic method in order to find out which of these is well suited for automatic contrast enhancement for satellite images of the ocean, obtained from a variety of sensors. All the techniques evaluated were based on
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Papers by Rekha Lakshmanan