Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification

• URL: http://arxiv.org/abs/2003.06615v1
• Date: Sat, 14 Mar 2020 12:11:23 GMT
• Title: Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification
• Authors: Sakshi Patel, Bharath K P and Rajesh Kumar Muthu
• Abstract summary: Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. We have also segmented and extracted the tumor part out of the brain using K-means algorithm.
• Score: 5.156484100374058
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses difficult; hence better localization of image pixels is required. Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. The contrast and brightness is enhanced in such a way that it does not lose its original information and the brightness is preserved. We compare the different equalization techniques in this paper; the techniques are critically studied and elaborated. They are also tabulated to compare various parameters present in the image. In addition we have also segmented and extracted the tumor part out of the brain using K-means algorithm. For classification and feature extraction the method used is Support Vector Machine (SVM). The main goal of this research work is to help the medical field with a light of image processing.

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