Title: A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification
Authors: Raju Pal; Mukesh Saraswat
Addresses: Department of Computer Science, Jaypee Institute of Information Technology, Noida, UP, India ' Department of Computer Science, Jaypee Institute of Information Technology, Noida, UP, India
Abstract: Automated histopathological image analysis is a challenging problem due to the complex morphological structure of histopathology images. Bag-of-features is one of the prominent image representation methods which has been successfully applied in histopathological image analysis. There are four phases in the bag-of-features method, namely feature extraction, codebook construction, feature encoding, and classification. Out of which feature encoding is one of the prime phases. In feature encoding phase, images are represented in terms of visual words before feeding into support vector machine classifier. However, the feature encoding phase of the bag-of-features framework considers the one feature to encode each image in terms of visual words due to which the system can not use the merits of other features. Therefore, to improve the efficacy of the bag-of-features framework, a new weighted two-dimensional vector quantisation encoding method is proposed in this work. The proposed method is tested on two histopathological image datasets for classification. The experimental results show that the combination of SIFT and ORB features with two dimensional vector quantisation encoding method returns 80.13% and 77.13% accuracy on ADL and Blue Histology datasets respectively which is better than other considered encoding methods.
Keywords: histolopathological image classification; bag-of-features; BOF; feature encoding.
International Journal of Intelligent Information and Database Systems, 2020 Vol.13 No.2/3/4, pp.150 - 171
Received: 01 May 2019
Accepted: 17 Jul 2019
Published online: 25 Aug 2020 *