Masters Theses

Abstract

"Medical image analysis has paved a way for research in the field of medical and biological image analysis through the applications of image processing. This study has special emphasis on nuclei segmentation from digitized histology images and pill segmentation. Cervical cancer is one of the most common malignant cancers affecting women. This can be cured if detected early. Histology image feature analysis is required to classify the squamous epithelium into Normal, CIN1, CIN2 and CIN3 grades of cervical intraepithelial neoplasia (CIN). The nuclei in the epithelium region provide the majority of information regarding the severity of the cancer. Segmentation of nuclei is therefore crucial. This paper provides two methods for nuclei segmentation. The first approach is clustering approach by quantization of the color content in the histology images uses k-means++ clustering. The second approach is deep-learning based nuclei segmentation method works by gathering localized information through the generation of superpixels and training convolutional neural network.

The other part of the study covers segmentation of consumer-quality pill images. Misidentified and unidentified pills constitute a safety hazard for both patients and health professionals. An automatic pill identification technique is essential to address this challenge. This paper concentrates on segmenting the pill image, which is crucial step to identify a pill. A color image segmentation algorithm is proposed by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. The algorithm manages to supersede the challenges due to various backgrounds and lighting conditions of consumer-quality pill images"--Abstract, page iii.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

Moss, Randy Hays, 1953-
Stoecker, William V.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Sponsor(s)

National Library of Medicine (U.S.)

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2017

Pagination

ix, 40 pages

Note about bibliography

Includes bibliographic references (pages 38-39).

Rights

© 2017 Sudhir Sornapudi, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11203

Print OCLC #

1022846575

Electronic OCLC #

1014181963

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