Texture classification using nonlinear color quantization: Application to histopathological image analysis

Sertel, Olcay; Kong, Jun; Lozanski, Gerard; Shana’ah, Arwa; Çatalyürek, Ümit V.; Saltz, Joel; Gürcan, Metin N.

doi:10.1109/icassp.2008.4517680

Cited by 53 publications

(50 citation statements)

References 6 publications

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“…However, this drawback is overcome using the SOM for quantization. In addition, the quantized image obtained using the SOM provided a more enhanced image compared to the quantized grey level image, thus resulting in more descriptive texture information [12].…”

Section: Color Texture Analysis Using Self-organizingmentioning

confidence: 99%

“…Taking this fact into account, we propose the use of a non-linear color quantization using self-organizing maps (SOM). We used the quantized image to construct the co-occurrence matrix that is used to compute low level color texture features [12]. By combining the statistical features constructed from the MBIR with the low level color texture features, the classification performance of the system is improved significantly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

Sertel

Kong

Çatalyürek

et al. 2008

J Sign Process Syst Sign Image Video Technol

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175

132

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Follicular lymphoma (FL) is a cancer of lymph system and it is the second most common lymphoid malignancy in the western world. Currently, the risk stratification of FL relies on histological grading method, where pathologists evaluate hematoxilin and eosin (H&E) stained tissue sections under a microscope as recommended by the World Health Organization. This manual method requires intensive labor in nature.Due to the sampling bias, it also suffers from inter-and intra-reader variability and poor reproducibility. We are developing a computer-assisted system to provide quantitative assessment of FL images for more consistent evaluation of FL. In this study, we proposed a statistical framework to classify FL images based on their histological grades. We introduced model-based intermediate representation (MBIR) of cytological components that enables higher level semantic description of tissue characteristics. Moreover, we introduced a novel color-texture analysis approach that combines the MBIR with low level texture features, which capture tissue characteristics at pixel level. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the accuracy of the system significantly. The implemented system can identify the most aggressive FL (grade III) with 98.9% sensitivity and 98.7% specificity and the overall classification accuracy of the system is 85.5%.

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Section: Color Texture Analysis Using Self-organizingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

Sertel

Kong

Çatalyürek

et al. 2008

J Sign Process Syst Sign Image Video Technol

Self Cite

175

132

View full text Add to dashboard Cite

show abstract

“…So the classification rate is improved where as wavelet transform is used for the decomposition of images which is sub band decoding for texture classification. A novel color texture classification approach [26] applied to computer-assisted grading of follicular lymphoma from whole-slide tissue samples. The digitized tissue samples of follicular lymphoma were classified into histological grades under a statistical framework.…”

Section: B Statistical Methodsmentioning

confidence: 99%

A Survey on Image Analysis based on Texture

Suresha

T²

2017

IJARCSSE

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Abstract-Texture classification is used in various pattern recognition applications. This paper contains survey on numerous techniques for texture description and different types of texture images. Texture description methods are categorized in to statistical, geometrical, model based, region based and transform based methods. These methodologies vary in their principles of describing and analyzing textures. For the datasets, Brodatz and SIPI texture dataset is the most popularly used dataset despite it being old and with limited samples, other datasets are less used. The study shows that the transform based methods and statistical methods are gaining popularity. The basic objective of this paper is to understand the research work carried out in the field of texture classification.

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“…Based on this method FL is stratified into three histological grades: FL grade I (0-5 centroblasts/HPF), FL grade II (6-15 centroblasts/HPF) and FL grade III (>15 centroblasts/HPF) ordered from the least to the most malignant subtypes, respectively. Further information about this problem and some previous work in this area can be found in the References [8][9][10][11][12][13][14][15][16][17].…”

Section: Problem 2: Counting Centroblasts From Histology Images Of Fomentioning

confidence: 99%

Pattern Recognition in Histopathological Images: An ICPR 2010 Contest

Gürcan

Madabhushi

Rajpoot

2010

Recognizing Patterns in Signals, Speech, Images and Videos

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Abstract. The advent of digital whole-slide scanners in recent years has spurred a revolution in imaging technology for histopathology. In order to encourage further interest in histopathological image analysis, we have organized a contest called "Pattern Recognition in Histopathological Image Analysis." This contest aims to bring some of the pressing issues facing the advance of the rapidly emerging field of digital histology image analysis to the attention of the wider pattern recognition and medical image analysis communities. Two sample histopathological problems are explored: counting lymphocytes and centroblasts. The background to these problems and the evaluation methodology are discussed.

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Texture classification using nonlinear color quantization: Application to histopathological image analysis

Cited by 53 publications

References 6 publications

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

A Survey on Image Analysis based on Texture

Pattern Recognition in Histopathological Images: An ICPR 2010 Contest

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