Home / Current Issue / Paper 1700401
Classifying Deviations In Medical Microscopic Images Using Evolutionary Analysis
Subject area: Science,Engineering and Technology · Area of research: Medical Mining
Abstract
Most of the patient diagnosis revolves around in identifying abnormalities in their respective medical images. These images are of various types, likely Ultrasound, CT Scan, MRI and microscopic images like bio-chemical slides, micro-biological slides & pathological slides. Few abnormalities are fractures, bad cells in blood, tumors, fungal identification etc. Finding the abnormal portions in these images needs expertise by the physician; this apt identification promotes and guarantees healthy medication by the physician or surgeon to patient. In medical microscopic images normal portions and abnormal portions are mixed together. None of the abnormal portions are related to abnormal and normal portions of image i.e. deviations are scattered among normal portions of image. These deviations are not present in some portions for specific area in the images. None of these deviations are overlapped nor can be grouped together into a single portion physically in the image. Deviations can be isolated along with normal portions of images. Identifying such deviations partially comes under classification and clustering. This project identifies deviations in Medical Microscopic images. These deviations can be identified visually which reveals about the presence of deviation but to know the percentage of deviation in a sample image is imperative. In-order to achieve this all deviations must be connected. This project connects all deviations using evolutionary analysis, includes the mixture functionalities of classification and clustering. Also this project uses BFS, DFS and random tracking for connecting deviations in the image.
Keywords
Deviation, Medical Images, Sickle cells
How to cite this paper
@article{1700401,
author = {Kakani Susmitha, Guntupalli Sai Tejaswi, Katakam Lakshmi Kala, Kakarla Precy Pranutha, Sudhir Tirumalasetty},
title = {Classifying Deviations In Medical Microscopic Images Using Evolutionary Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {1},
number = {9},
pages = {233-240},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1700401.pdf},
abstract = {Most of the patient diagnosis revolves around in identifying abnormalities in their respective medical images. These images are of various types, likely Ultrasound, CT Scan, MRI and microscopic images like bio-chemical slides, micro-biological slides & pathological slides. Few abnormalities are fractures, bad cells in blood, tumors, fungal identification etc. Finding
the abnormal portions in these images needs expertise by the physician; this apt identification promotes and guarantees healthy medication by the physician or surgeon to patient. In medical microscopic images normal portions and abnormal portions are mixed together. None of the abnormal portions are related to abnormal and normal portions of image i.e. deviations are scattered among normal portions of image. These deviations are not present in some portions for specific area in the images. None of these deviations are overlapped nor can be grouped together into a single portion physically in the image. Deviations can be isolated along with normal portions of images. Identifying such deviations partially comes under classification and clustering. This project identifies deviations in Medical Microscopic images. These deviations can be identified visually which reveals about the presence of deviation but to know the percentage of deviation in a sample image is imperative. In-order to achieve this all deviations must be connected. This project connects all deviations using evolutionary analysis, includes the mixture functionalities of
classification and clustering. Also this project uses BFS, DFS and random tracking for connecting deviations in the image. },
keywords = {Deviation, Medical Images, Sickle cells},
month = {March},
}