Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching

Wiliem, Arnold; Sanderson, Conrad; Wong, Yongkang; Hobson, Peter; Minchin, Rodney F.; Lovell, Brian C.

doi:10.1016/j.patcog.2013.10.014

Cited by 53 publications

(30 citation statements)

References 33 publications

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“…By utilizing circular rings, histograms computed from each ring are rotationally invariant. Previous work [10] has used a technique similar to this for cell classification. However, here we use superpixel visual words and describe a generalisable multi-level pyramid applicable to other computer vision tasks.…”

Section: Rotation Invariant Superpixel Pyramidmentioning

confidence: 99%

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Akbar

Jordan

Thompson

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions. A superpixel representation retains information about visual structures such as cellular compartments, connective tissue, lumen and fatty tissue without having to commit to semantic segmentation at this level. In order to localize tumor in large images, a rotation invariant spatial pyramid representation is proposed using bags-of-superpixels. The method is evaluated on expert-annotated oestrogen-receptor stained TMA spots and compared to other superpixel classification techniques. Results demonstrate that it performs favorably.

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Section: Rotation Invariant Superpixel Pyramidmentioning

confidence: 99%

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Akbar

Jordan

Thompson

et al. 2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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“…Different types of image descriptors such as global, local, region based, texture and microscopic features have been used. Wiliem et al [1], proposed a bag of 256 visual words. IIF images were represented as a histogram of occurrences of these visual words in the image.…”

Section: Introductionmentioning

confidence: 99%

Classification of indirect immunofluorescence images using thresholded local binary count features

Susaiyah

Pathan

Swaminathan

2016

Current Directions in Biomedical Engineering

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Abstract:Computer aided classification of HEp-2 cell based indirect immunofluorescence (IIF) images is a recommended procedure for standardising autoimmune disease diagnostics. In this work a novel feature, the thresholded local binary count (TLBC) has been proposed to classify IIF images into one among six classes. The TLBC is rotational invariant and is insensitive to pixel quantization noise. It characterizes the local binary gray scale pixel information in an image. The proposed feature along with global features such as area, entropy, illumination level and mean intensity, when classified using a support vector machine gave an accuracy of 86%. This feature could help in improving the diagnostics of autoimmune diseases which is highly clinically significant.

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“…The framework of SPM based on BoW has been successfully applied to image classification [12,13], and in recent years, it has been improved for HEp-2 cell classification [14,15]. It seems to be suitable for the HEp-2 cell classification task.…”

Section: Introductionmentioning

confidence: 99%

Automated classification for HEp-2 cells based on linear local distance coding framework

Lin

et al. 2015

J Image Video Proc.

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The occurrence of antinuclear antibodies (ANAs) in patient serum has significant relation to some specific autoimmune diseases. Indirect immunofluorescence (IIF) on human epithelial type 2 (HEp-2) cells is the recommended methodology for detecting ANAs in clinic practice. However, the currently practiced manual detection system suffers from serious problems due to subjective evaluation. In this paper, we present an automated system for HEp-2 cells classification. We adopt a bag-of-words (BoW) framework which has shown impressive performance in image classification tasks because it can obtain discriminative and effective image representation. However, the information loss is inevitable in the coding process. Therefore, we propose a linear local distance coding (LLDC) method to capture more discriminative information. Our LLDC method transforms original local feature to more discriminative local distance vector by searching for local nearest few neighbors of the local feature in the class-specific manifolds. The obtained local distance vector is further encoded and pooled together to get salient image representation. The LLDC method is combined with the traditional coding methods to achieve higher classification accuracy. Incorporated with a linear support vector machine classifier, our proposed method demonstrated its effectiveness on two public datasets, namely, the International Conference on Pattern Recognition (ICPR) 2012 dataset and the International Conference on Image Processing (ICIP) 2013 training dataset. Experimental results show that the LLDC framework can achieve superior performance to the state-of-the-art coding methods for staining pattern classification of HEp-2 cells.

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Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching

Cited by 53 publications

References 33 publications

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Classification of indirect immunofluorescence images using thresholded local binary count features

Automated classification for HEp-2 cells based on linear local distance coding framework

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