Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis

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%. LAY DESCRIPTION: Childhood medulloblastoma is a case of childhood brain tumour that requires high attention due to a 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 (W.H.O), it has four subtypes (desmoplastic, classic, nodular, and large). Classification is done in two levels: i) normal and abnormal 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 is a model for the automatic classification of the samples. The tissue samples obtained post-operation by doctors are converted into images, and then necessary algorithms are applied so that certain features describing each group of the image are known and studied for classification. Later these images are classified using the image features into the subtypes of abnormal samples.