Classification of normal and abnormal sperm from suspension of sprague dawley rat sperm

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dc.contributor.authorAlias, Mohd Fauzi
dc.date.accessioned2014-11-03T02:15:15Z
dc.date.available2014-11-03T02:15:15Z
dc.date.issued2009
dc.descriptionMasteren_US
dc.description.abstractAs of now, the analysis of sperm such as counting and detection processes are still operated manually. Even though the results obtained are of high quality, errors still emerge. False detection in sperm analysis must be minimized as possible. Therefore, the current study focuses on developing a Sprague Dawley rat sperm classification system to assist the detection process by pathologist. The system has the ability to classify the Sprague Dawley rat sperm into three classes namely normal, hookless abnormal and banana shape abnormal based on the morphological characteristics of the sperm’s head. The proposed system employs digital image processing technique to classify sperm into normal and abnormal classes as well as neural network to further classify sperm into normal, hookless abnormal and banana shape abnormal. Several digital image processing techniques have been integrated such as segmentation, hole filling and template matching. In segmentation process, this research proposes two new segmentation algorithms called as Threshold Doubled Value (TDV) and Modified Moving K-Mean (MMKM). These algorithms have been proven to give better segmentation results as compared to the conventional algorithms. This research also proposes a new implementation process for chain code method to fill holes and noises which occur in segmented sperm’s head image. In the sperm classification, template matching technique using cross correlation algorithm successfully produces 99.79% of accuracy. However, the sperm classification using the Hybrid Multilayered Perceptron (HMLP) network trained with Modified Recursive Prediction Error (MRPE) algorithm achieved a higher accuracy by 100%. The HMLP network further classifies the rat sperm into three classes with high accuracy at 94.62%. This research also proposes three significant features of sperm image to be used as input data to the HMLP network namely matching percentage, opened degree and width of the bend.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/262
dc.language.isoenen_US
dc.subjectBiological Scienceen_US
dc.subjectNormal spermen_US
dc.subjectAbnormal spermen_US
dc.subjectRat spermen_US
dc.titleClassification of normal and abnormal sperm from suspension of sprague dawley rat spermen_US
dc.typeThesisen_US
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