Research On Segmentation, Texture Extraction And Classification Methods For Cell Images

This thesis is focused on some issues related to segmentation,texture extraction andclassification of cell images. These issues mainly include a method to accurately extractboth the nucleus and cytoplasm, two new texture extraction methods, as well as a fusionmethod of different kinds of features. In addition, using ELM to deal with the imbalanceddataset classification problem, which is common in medical data, is also discussed in thisthesis. The main contributes can be exhibited by the following aspects:1. A radiating GVF Snake (RGVF) model is proposed aiming at accurate extrac-tion of both the nucleus and cytoplasm from a single-cell image. GVF Snake model isa widely used contour tracking method in image processing. However, when used toextract the nuclei and cytoplasm from cell images, GVF Snake may be easily absorbedto wrong positions due to the fact that (1) the boundaries between the cytoplasm and thebackground are oftenquite obscure;(2) alot of inferences exist nearthe edgeof the nucleiand the cytoplasm, including inflammatory cells, blood cells and other noises. To solvethese problems, RGVF involves a new edge map computation method and a stack-basedrefinement, and is thus robust to contaminations and can effectively locate the obscureboundaries. The boundaries can also be correctly traced even if there are interferencesnear the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset,which contains917images show the effectiveness of the proposed algorithm.2. A novel texture extraction method based on Gabor filters is proposed. Texturefeatures play important roles in cell classifications. The cell image is first decomposedby convolving with multi-scale and multi-orientation Gabor filters, then separated intoseveral blocks. The Block Feature Vector (BFV) can be obtained through statistical tech-niques. The Total Feature Vector (TFV) of the whole image is then constructed by conju-gating the BFVs in row column order. In the classification stage, a robust classificationmethod which performs multi-class classification is built based on many two-class clas-sifiers using voting mechanism. Before each two-class classifier, a feature extractionmodule adaptively selects the most important features. The results compared with thepublished results on Yale face database verify the validity of the proposed method. Thestaining pattern classification results on HEp-2cell dataset also prove that the proposemethod is effective. 3. A novel texture extraction method named Maximum Entropy based Local Mul-tiple Pattern (MELMP) is proposed. Local multiple patterns(LMP) has been proved tobe an efficient and robust texture extraction method. However, the thresholds have to beset manually and the the feature dimension is quite high in LMP. To solve these prob-lems, a maximum entropy based thresholding scheme, which computes the thresholds bydividing the intensity difference histogram of an image equally, is adopted, and the split-concatenate encoding is used to form shorter and more effective feature vectors. Exper-imental results on four test suits with an SVM classifier show that the proposed methodachieves overall better performances than both LBP and LMP in texture classification.The staining pattern classification results on HEp-2cell dataset are also very satisfying.4. A feature fusion framework based on posteriori probability classifier and Ad-aBoost.M1framework is introduced. How to fuse different features together is quiteimportant in achieving better cell image classification results. In this thesis:(1) withineach boosting round, several posterior probability classifiers are trained corresponding todifferent descriptors, and then combined to an integrated classifier;(2) AdaBoost.M1ismodifiedtoenhancetheperformanceoftheintegratedclassifiers. ExperimentalresultsonHEp-2cell dataset show the proposed method is effective and can significantly improvethe classification accuracy.5. Two strategies to deal with imbalanced classification are proposed, namely cost-sensitive ELM (CS-ELM) and ELM based cost-sensitive AdaBoost (ELM-AdaCx). First,cost-sensitive information is introduced into the training process of ELM to form CS-ELM. A genetic algorithm (GA) is utilized to find the optimal weights. Second, the pro-posed CS-ELM is utilized as the meta classifier and embedded into a cost-sensitive Ad-aBoost.M1frameworktoformELM-AdaCx. Experimentalresultson19datasetsfromtheKEEL repository show that the proposed strategies could achieve more balanced resultsthan the basic ELM.