Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

Vestergaard

IEEE Trans. Med. Imaging

2014

We present a new method for automatic classification of indirect immunoflourescence images of HEp-2 cells into different staining pattern classes. Our method is based on a new texture measure called shape index histograms that captures second-order image structure at multiple scales. Moreover, we introduce a spatial decomposition scheme which is radially symmetric and suitable for cell images. The spatial decomposition is performed using donut-shaped pooling regions of varying sizes when gathering histogram contributions. We evaluate our method using both the ICIP 2013 and the ICPR 2012 competition datasets. Our results show that shape index histograms are superior to other popular texture descriptors for HEp-2 cell classification. Moreover, when comparing to other automated systems for HEp-2 cell classification we show that shape index histograms are very competitive; especially considering the relatively low complexity of the method.

Section: A Related Workmentioning

confidence: 88%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

Vestergaard

IEEE Trans. Med. Imaging

2014

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

“…2 sketches how the three regions are divided. Our proposed approach is inspired from the Cell Pyramid Matching (CPM) descriptor which was proposed to address cell classification in fluorescence images [1]. Unlike the CPM descriptor however, we extract a covariance descriptor from each region.…”

Section: Whole Regionmentioning

confidence: 99%

“…Recently there has been growing interest in applying image analysis to pathology test images [1,2,3,4]. More precisely, Computer Aided Diagnosis (CAD) systems were developed to automatically provide analysis based on the input images.…”

Section: Introductionmentioning

confidence: 99%

A multiple covariance approach for cell detection of Gram-stained smears images

Crossman

Wiliem

Finucane

et al. 2015

Self Cite

Microscope examination of Gram stained clinical specimens is used for aiding the diagnosis of patients with infectious diseases. In high volume pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. Unfortunately, despite the great benefits offered from the application of Computer Aided Diagnosis (CAD) systems, to our knowledge, the highest automation stage for Gram stained slide analysis is only at the preanalytical process. This paper takes the first steps towards the application of computer vision to direct smear, Gram stained images. To that end, we present a novel Gram stain image dataset. In addition, we also propose a multiple covariance approach for leukocyte and epithelial cell detection in Gram stain images. Each covariance matrix represents a particular image region characterising the cell's deformed structure. As covariance matrices form points on an Symmetric Positive Definite (SPD) manifold, the traditional Euclidean-based analysis cannot be used. As such, we first map the manifold points into the Reproducing Kernel Hilbert Space (RKHS). The analysis is done via a novel kernel similarity function that allows comparison between sets of covariance matrices. The proposed approach is contrasted, in the proposed dataset, with two recent state of the art methods in pedestrian detection: Histogram Of Gradient (HOG) and the traditional single covariance matrix approach. We found that the proposed approach outperformed both of these methods.

A bag of cells approach for antinuclear antibodies HEp‐2 image classification

et al. 2014

Self Cite

The antinuclear antibody (ANA) test via indirect immunofluorescence applied on Human Epithelial type 2 (HEp-2) cells is a pathology test commonly used to identify connective tissue diseases (CTDs). Despite its effectiveness, the test is still considered labor intensive and time consuming. Applying image-based computer aided diagnosis (CAD) systems is one of the possible ways to address these issues. Ideally, a CAD system should be able to classify ANA HEp-2 images taken by a camera fitted to a fluorescence microscope. Unfortunately, most prior works have primarily focused on the HEp-2 cell image classification problem which is one of the early essential steps in the system pipeline. In this work we directly tackle the specimen image classification problem. We aim to develop a system that can be easily scaled and has competitive accuracy. ANA HEp-2 images or ANA images are generally comprised of a number of cells. Patterns exhibiting in the cells are then used to make inference on the ANA image pattern. To that end, we adapted a popular approach for general image classification problems, namely a bag of visual words approach. Each specimen is considered as a visual document containing visual vocabularies represented by its cells. A specimen image is then represented by a histogram of visual vocabulary occurrences. We name this approach as the Bag of Cells approach. We studied the performance of the proposed approach on a set of images taken from 262 ANA positive patient sera. The results show the proposed approach has competitive performance compared to the recent state-of-the-art approaches. Our proposal can also be expanded to other tests involving examining patterns of human cells to make inferences. V C 2014 International Society for Advancement of Cytometry