ISSN 2394-5125
 

Review Article 


AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES

TELAGARAPU PRABHAKAR*, POONGUZHALI.

Abstract
Breast lesion identification as early is a dynamic role to reduce the death rate. Mammography is a technique for breast
cancer finding, which are harmful, and can be embarrassing for the younger women. In this paper focusing on automatic
detection and feature selection to classify the breast lesions from ultrasound images by using Morphological features.
Here filtering technique is used for the reduction of noise in the image before automatic detection. An active contour
procedure is used for lesion segmentation. From ROI of Tetrolet filtered and Input Image morphological features are
extracted. These features are reduced by feature selection algorithm then given to the SVM Classifier. From the results
observed that the SVM classifier with Tetrolet morphological features outperforms than without filter morphological
features with 85.45 % accuracy, 85.21% sensitivity and specificity of 88.89%.

Key words: Breast Ultrasound image, Speckle reduction, Morphological feature extraction, feature selection and classification


 
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How to Cite this Article
Pubmed Style

TELAGARAPU PRABHAKAR, POONGUZHALI. AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. JCR. 2020; 7(3): 162-172. doi:10.31838/jcr.07.03.31


Web Style

TELAGARAPU PRABHAKAR, POONGUZHALI. AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. http://www.jcreview.com/?mno=93194 [Access: June 02, 2021]. doi:10.31838/jcr.07.03.31


AMA (American Medical Association) Style

TELAGARAPU PRABHAKAR, POONGUZHALI. AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. JCR. 2020; 7(3): 162-172. doi:10.31838/jcr.07.03.31



Vancouver/ICMJE Style

TELAGARAPU PRABHAKAR, POONGUZHALI. AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. JCR. (2020), [cited June 02, 2021]; 7(3): 162-172. doi:10.31838/jcr.07.03.31



Harvard Style

TELAGARAPU PRABHAKAR, POONGUZHALI (2020) AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. JCR, 7 (3), 162-172. doi:10.31838/jcr.07.03.31



Turabian Style

TELAGARAPU PRABHAKAR, POONGUZHALI. 2020. AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. Journal of Critical Reviews, 7 (3), 162-172. doi:10.31838/jcr.07.03.31



Chicago Style

TELAGARAPU PRABHAKAR, POONGUZHALI. "AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES." Journal of Critical Reviews 7 (2020), 162-172. doi:10.31838/jcr.07.03.31



MLA (The Modern Language Association) Style

TELAGARAPU PRABHAKAR, POONGUZHALI. "AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES." Journal of Critical Reviews 7.3 (2020), 162-172. Print. doi:10.31838/jcr.07.03.31



APA (American Psychological Association) Style

TELAGARAPU PRABHAKAR, POONGUZHALI (2020) AUTOMATIC DETECTION AND FEATURE SELECTION TO CLASSIFY THE BREAST LESIONS ON ULTRASOUND IMAGE USING MORPHOLOGICAL FEATURES. Journal of Critical Reviews, 7 (3), 162-172. doi:10.31838/jcr.07.03.31