Date of Award

Fall 2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical & Computer Engineering

Program/Concentration

Electrical and Computer Engineering

Committee Director

Jiang Li

Committee Member

Dimitrie Popescu

Committee Member

Chunsheng Xin

Call Number for Print

Special Collections LD4331.E55 J368 2014

Abstract

Drosophila melanogaster is a dominant model organism for studying the function of animal genes in initial stages of embryogenesis. Usually, images containing Drosophila gene expression patterns are captured at different developmental stages to study the interconnection of animal genes. To achieve most biologically meaningful results, gene expression images from a similar stage should be compared. Currently, biologists manually classify embryos in images into different stages, which is time intensive and infeasible for current massively produced gene expression images. Therefore, there is a need to develop an automatic system for the annotation.

Gene expression information in embryo images usually appears as blue texture patterns, which are mingled together with the embryo stage texture information in the image. When manually annotating an image, an experienced biologist can easily classify an embryo to its correct stage based on stage texture and ignore blue gene expression patterns. For automatic annotation, gene expression and embryo stage texture information must be separated. In this thesis, we investigated two data completion techniques, matrix completion and neural network, as image preprocessing steps to eliminate gene expression information in embryo images. The preprocessed images then went through feature extraction and classification steps for automatic embryo stage annotation. Experimental results show that the preprocessing steps improved annotation performance by 4.5% and 3.63% with matrix completion and a neural network, respectively.

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DOI

10.25777/cy2n-9p62

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