Childhood medulloblastoma is a case of a childhood brain tumour that requires close attention due to the low survival rate. Effective prognosis depends a lot on accurate detection of its subtype. The present study proposes a texture-based computer-aided categorization of childhood medulloblastoma samples. According to the World Health Organization, it has four subtypes (desmoplastic, classic, nodular and large). Classification is done in two levels: (i) normal and abnormal and (ii) its four subtypes. The system is evaluated on indigenous patient samples collected from the region. The main objective of database generation is to create a data set of childhood medulloblastoma samples since there exists no available benchmark data set. The proposed framework for automated classification is based on the architectural property and the distribution of cells. Five texture features were extracted for the feature set, namely: grey-level co-occurrence matrix, grey-level run length matrix, first-order histogram features, local binary pattern and Tamura features. The performance of each feature set was evaluated, both individually and in combinations, using five different classifiers. Fivefold cross-validation was used for training and testing the data set. Experiments on both individual feature sets and combinations (best-2, best-3, best-4 and all-5) of feature sets were evaluated based on the accuracy of performance. It was revealed that the combined best-4 feature set resulted in the highest accuracy of 91.3%. The precision, recall and specificity were 0.913, 0.913 and 0.97, respectively. Significantly, it implied that the all-5 feature set is not necessary to have a useful classification. Feature reduction by principal component analysis resulted in increased accuracy of 96.7%.

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Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approach of pathological subtype classification using biological interpretable and computer-aided textural features is forwarded. The classifier for accurate features prediction is built purely on the feature set obtained by segmentation of the ground truth cells from the original histological tissue images, marked by an experienced pathologist. The work is divided into five stages: marking of ground truth, segmentation of ground truth images, feature extraction, feature reduction and finally classification. Kmeans colour segmentation is used to segment out the ground truth cells from histological images. For feature extraction we used morphological, colour and textural features of the cells followed by feature reduction using Principal Component Analysis. Finally both binary and multiclass classification is done using Support Vector Method (SVM). The classification was compared using six different classifiers and performance was evaluated employing five-fold cross-validation technique. The accuracy achieved for binary and multiclass classification before applying PCA were 95.4 and 62.1% and after applying PCA were 100 and 84.9% respectively. The run-time analysis are also shown. Results reveal that this technique of cell level classification can be successfully adopted as architectural view can be confusing. Moreover it conforms substantially to the pathologist's point of view regarding morphological and colour features, with the addition of computer assisted texture feature.

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