Exploration and Performance Assessment of Efficient Histopathology Image Pre-Processing and Segmentation Techniques for Breast Cancer Prediction
Vandana Kate1, Pragya Shukla2

1Vandana Kate*, Institute of Engineering and Technology, Research Scholar, DAVV Indore.
2Pragya Shukla, Instutute of Engineering and Technology, Associate, Prof., DAVV Indore.

Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 351-357 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6349129219/2019©BEIESP | DOI: 10.35940/ijitee.B6349.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Evaluation of features after segmentation is a significant process in image processing, especially in the medical field. Medical imaging technologies are widely utilized in clinical diagnosis to guide therapeutic and surgical decisions and to monitor disease advancement, understanding occurrence and reoccurrence of infection and view treatment response. Among all cancer kinds, Breast Cancer (BC) now-a-days has turn out to be a common form of cancer amongst ladies around the world and is the second common cause of most cancers deaths. At present, there aren’t any powerful methods to save you and remedy breast cancer, because its cause is not absolutely known. Early detection is the most effective way to enhance breast cancer survivals and might deliver a better hazard of full healing. The main motive of this paper is to implement various efficient image segmentation techniques after preprocessing histopathology BC images of different magnification level and exhaustively compare them to achieve best results. We analyze the outcomes of widely used edge detection based image segmentation methods such as Adaptive K-Means, Multi Class Fuzzy C-Means, Canny, Gabor filter and Homogeneity based PSO and evaluate them via metrics which includes accuracy, precision, recall and F-degree. Various preprocessing strategies like histogram equalization (HE), Adaptive equalization (AE) and Contrast Stretching (CS) are compared and used to enhance the performance of above segmentation methods. Contrast Improvement Index (CII) is chosen as a selection criteria for preprocessed image. Finally one vs. all multiclass SVM classifier is used for BC image classification. Our implementation uses breast cancer dataset having two classes as benign and malignant each in turn having four sub-classes. Images of different magnification levels such as 40x, 100x, 200x and 400x, are considered for BC detection. 
Keywords: Breast Cancer, Classification, Image Segmentation, Multiclass SVM
Scope of the Article: Classification