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Automatic Image Segmentation for Hair Masking: two Methods

We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-254258
Date January 2019
CreatorsVestergren, Sara, Zandpour, Navid
PublisherKTH, Skolan för elektroteknik och datavetenskap (EECS)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationTRITA-EECS-EX ; 2019:142

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