HEp-2 cells classification via sparse representation of textural features fused into dissimilarity space

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: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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

“…In real signal processing, in order to improve the processing effect and speed, such sparse data expressions are always needed. Replacing the original data by sparse approximation can not only reduce the signal processing cost fundamentally, but also greatly improve the compact efficiency [6,7]. …”

Section: Sparse Decomposition Algorithmmentioning

confidence: 99%

A Sparse Representation Image Denoising Method Based on Orthogonal Matching Pursuit

2015

TELKOMNIKA

Image denoising is an important research aspect in the field of digital image processing, andsparse representation theory is also one of the research focuses in recent years Keywords: Image Denoising, OMP (Orthogonal Matching Pursuit), Sparse RepresentationCopyright © 2015 Universitas Ahmad Dahlan. All rights reserved. IntroductionWhen people receiving outside information, 80% are visual information. Digital image is the major source of visual information. However, in the meantime, while we receiving information of the image, there are inevitably interference of both internal factors and external factors, which makes the image contain many noises and makes the received information incomplete or even incorrect [1]. Noise commonly refers to the useless information. In the process of image processing, it is required to effectively restrict noise and improve the quality and visual effect of the image, which can not only improve correct judgment for the image, but also is very meaningful for the after-processing of the image [2].How to denoising the image is one of the research focuses in recent years. Signal processing method changes from orthogonal transform to wavelet transform, then to multi-scale transform. In recent years, along with the development of compressed sensing technology, sparse representation theory has become a new research direction in the field of image denoising. Sparse model refers to describe the exist signal only with very little linear set in basic dictionary [3]. It is well known that ordinary images can be sparse representation in certain transform domain, thus to transfer the image to this transform domain. And the fortunate thing is that noise cannot be sparse represented in transform domain. Based on this premise, sparse representation theory can effectively delete the noise in the image. The image over complete signal sparse representation theory is first put forward by Mallat in1993, and the adopted image sparse decomposition algorithm is the Matching Pursuit (MP) algorithm put by him. Of course, until now this theory is still not completely mature, and requires further study and discussion. However, it is another new thought and direction after previous image denoising theories [4,5]. This paper mainly based on sparse representation theory to explore the image denoising problem. It first conducts a brief introduction of noisy image and sparse representation theory, then explains the main process and idea of OMP. Based on the above mentioned research theories and technologies, it puts forward the design process of the sparse representation image denoising method based on OMP. The last part of this paper isexperiment design and result analysis.

2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images

“…In particular, several works rely on the use of local descriptors, like the well-known local binary pattern (LBP) [3] (or its variants) and the SIFT descriptor [4]. This is the case for example of [5] where these descriptors are both used and combined with other ad hoc ones, or of [6] where the Co-occurrence of Adjacent LBP (CoALBP) has been considered in combination with SIFT. It is worth noting that CoALBP was used by the top competitor of the contest [1].…”

Section: Introductionmentioning

confidence: 99%

Biologically-Inspired Dense Local Descriptor for Indirect Immunofluorescence Image Classification

Gragnaniello¹,

Sansone²,

Verdoliva³

2014

This work deals with the design of a classification method for cells extracted from Indirect Immunofluorescence images. In particular, we propose to use a dense local descriptor invariant both to scale changes and to rotations in order to classify the six categories of staining patterns of the cells. The descriptor is able to give a compact and discriminative representation and combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag of Words is finally used to perform classification and experimental results show very good performance.