Abstract
Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.
Highlights
It is a process in which an image is given to a system and searching is performed on the complete dataset of images to find the similar images based oncolor, size, texture
Histograms are a type of bar plot for numeric data that group the data into bins
The Gray-Level Co-Occurrence Matrix (GLCM) functions characterize the texture of an image by calculating how often pairs of pixels with specific values and in a specified spatial relationship occur in an image, creating a GLCM, and extracting statistical measures from this matrix[3]
Summary
It is a process in which an image is given to a system and searching is performed on the complete dataset of images to find the similar images based oncolor, size, texture. Http://www.ijetmr.com©International Journal of Engineering Technologies and Management Research [181]
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