Accurate HEp-2 cell classification based on sparse bag of words coding.

Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW). In particular, SURF (Speeded Up Robust Features) and SIFT (Scale-invariant feature transform) features are specially integrated to work in a complementary fashion. This method helps greatly improve the cell classification accuracy. Additionally, a hierarchical max-pooling method is proposed to aggregate the local sparse codes in different layers to provide final feature vector. Furthermore, various parameters of the dictionary learning including the dictionary size, the learning iteration number, and the pooling strategy is also investigated. Experiments conducted on publicly available datasets show that the proposed technique clearly outperforms state-of-the-art techniques in cell and specimen levels.

3D reconstruction of neurons in electron microscopy images.

With the prevalence of brain-related diseases like Alzheimer in an increasing ageing population, Connectomics, the study of connections between neurons of the human brain, has emerged as a novel and challenging research topic. Accurate and fully automatic algorithms are needed to deal with the increasing amount of data from the brain images. This paper presents an automatic 3D neuron reconstruction technique where neurons within each slice image are first segmented and then linked across multiple slices within the publicly available Electron Microscopy dataset (SNEMI3D). First, random Forest classifier is adapted on top of superpixels for the neuron segmentation within each slice image. The maximum overlap between two consecutive images is then calculated for neuron linking, where the adjacency matrix of two different labeling of the segments is used to distinguish neuron merging and splitting. Experiments over the SNEMI3D dataset show that the proposed technique is efficient and accurate.