Open AccessCancer cell Detection using FMM Compressed Images
Author(s) -
Alamuru Gandla Bhargavi,
H N Nandini,
S Akshay
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1038.078219
Subject(s) - computer science , artificial intelligence , computer vision , cell counting , dilation (metric space) , pixel , task (project management) , edge detection , canny edge detector , grayscale , image processing , image (mathematics) , cell , mathematics , biology , genetics , combinatorics , cell cycle , management , economics
Counting a lymphocytes is bit a time consuming and tedious task and also not that interesting but we should not neglect the task because doctor should get an accurate report about a patient so that it will help to understand a doctor more about a patient and sometimes the manual counting of lymphocytes may also lead to improper / incorrect count of a lymphocyte which affect in analyzing a patient so we should not neglect the task. Because of these disadvantages we are developing an automated tool to count the number of lymphocytes using image processing. Initially image is converted into gray scale to get the maximum accuracy in the result, and then the image is compressed so that it can be stored in a minimal storage space, then edge detection of a cell is done through canny detection process to extract the counter boundary of the blood cell, then dilation and erode is applied to enlarge the interested cell and contract the non-interested cells by using a morphological characteristics ,then watershed algorithm is applied to segment the cytoplasm and nucleus from the blood cell. Finally counting of lymphocytes is performed.