Christopher Ohagwu

By Kenneth K. Agwu and Christopher C. Ohagwu

This chapter describes histogram-based texture characterization and classification of brain tissue in CT images of stroke patients using a case study. It explored texture analysis in medical imaging. In the case study, two radiologists independently inspected non-contrast CT images of 164 stroke to identify and categorize brain tissue into normal, ischaemic and haemorrhagic strokes. Four regions of interest (ROIs) in each CT slice with lesion were selected for analysis; two each represented the lesion and normal tissue. Histogram texture parameters were calculated for them. Raw data analysis identified parameters that discriminated between normal brain tissue, ischaemic and haemorrhagic stroke lesions. The artificial neural network (ANN) and k-nearest neighbour (k-NN) algorithms were used to classify the ROIs into normal tissue, ischaemic and haemorrhagic lesions using the radiologists’ categorization as the gold standard, and further analysed using the ROC curve. Three parameters namely mean, 90 and 99 percentiles discriminated between normal brain tissue, ischaemic and haemorrhagic stroke lesions. With ANN and k-NN, the weighted sensitivity and specificity were above 0.9 while the false positive and false negative rates were negligible. The characterization and classification of brain tissue using histogram parameters were satisfactory and may be suitable for automated diagnosis of stroke.

Part of the book: Pattern Recognition