Micro‐structural tissue analysis for automatic histopathological image annotation

Díaz, Gabriel; Romero, Eduardo

doi:10.1002/jemt.21063

Cited by 20 publications

(19 citation statements)

References 52 publications

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“…Because of the good performance in that task, the BoVW approach quickly became popular and began to expand into other fields of computer vision such as IC 4,5,7 , image retrieval 6,16 , object recognition 24 and human-activity recognition 19 . The success of this approach could be easily explained through an analogy with the BoW used in text classification tasks.…”

Section: Related Workmentioning

confidence: 99%

“…The descriptor is built merging the 64 coefficients from each one of the three channels. We considered these features because in previous studies they have outperformed alternative representations (including SIFT features and rawpatches) 4,5,7 . However, other types of feature-descriptors could be considered as well.…”

Section: Construction Of the Visual Words Codebookmentioning

confidence: 99%

“…For evaluating the use of BoVN approach we consider a dataset of histopathology images, annotated by a pathologist, describing the presence of one architectural or morphological features, and pathological tissues 5,7 . Images correspond to RGB color images at 10X magnification stained with Hematoxilyn-eosin (H&E) from skin tissues diagnosed as healthy (collagen, epidermis, hair follicle, eccrine glands, sebaceous glands and inflammatory infiltrate), or by presence of basalcell carcinoma (BCC) (which is the only one related with cancer diagnosis).…”

Section: Experimental Settingsmentioning

confidence: 99%

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Bag-of-visual-ngrams for histopathology image classification

et al. 2013

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This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image classification (IC) of histophatology images. This representation is one of the most used approaches in several high-level computer vision tasks. However, the BoVW representation has an important limitation: the disregarding of spatial information among visual words. This information may be useful to capture discriminative visual-patterns in specific computer vision tasks. In order to overcome this problem we propose the use of visual n-grams. N-grams based-representations are very popular in the field of natural language processing (NLP), in particular within text mining and information retrieval. We propose building a codebook of n-grams and then representing images by histograms of visual n-grams. We evaluate our proposal in the challenging task of classifying histopathology images. The novelty of our proposal lies in the fact that we use n-grams as attributes for a classification model (together with visual-words, i.e., 1-grams). This is common practice within NLP, although, to the best of our knowledge, this idea has not been explored yet within computer vision. We report experimental results in a database of histopathology images where our proposed method outperforms the traditional BoVWs formulation.

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Section: Related Workmentioning

confidence: 99%

Section: Construction Of the Visual Words Codebookmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

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Bag-of-visual-ngrams for histopathology image classification

et al. 2013

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“…It turns out that the limiting value of ε becomes 1 + and B becomes a square of side length ε in Eq. (2).…”

Section: Multifractal Analysis On Digital Imagementioning

confidence: 99%

“…Figure 1 shows two prostate histopathology images of cancer and non-cancer regions. In the last two decades, medical diagnosis routines have been partially replaced by Computer Aided Diagnosis (CAD) systems [2]. Particularly, the image analysis based CAD systems observe the structural behavior of the texture or cellular/sub-cellular components using mathematical feature descriptors and discriminate the images according to a quantitative scale.…”

Section: Introductionmentioning

confidence: 99%

Classification of Prostate Histopathology Images Based on Multifractal Analysis

Atupelage

Nagahashi

Yamaguchi

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYHistopathology is a microscopic anatomical study of body tissues and widely used as a cancer diagnosing method. Generally, pathologists examine the structural deviation of cellular and sub-cellular components to diagnose the malignancy of body tissues. These judgments may often subjective to pathologists' skills and personal experiences. However, computational diagnosis tools may circumvent these limitations and improve the reliability of the diagnosis decisions. This paper proposes a prostate image classification method by extracting textural behavior using multifractal analysis. Fractal geometry is used to describe the complexity of self-similar structures as a non-integer exponent called fractal dimension. Natural complex structures (or images) are not self-similar, thus a single exponent (the fractal dimension) may not be adequate to describe the complexity of such structures. Multifractal analysis technique has been introduced to describe the complexity as a spectrum of fractal dimensions. Based on multifractal computation of digital imaging, we obtain two textural feature descriptors; i) local irregularity: α and ii) global regularity: f (α). We exploit these multifractal feature descriptors with a texton dictionary based classification model to discriminate cancer/non-cancer tissues of histopathology images of H&E stained prostate biopsy specimens. Moreover, we examine other three feature descriptors; Gabor filter bank, LM filter bank and Haralick features to benchmark the performance of the proposed method. Experiment results indicated that the performance of the proposed multifractal feature descriptor outperforms the other feature descriptors by achieving over 94% of correct classification accuracy.

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Bag of Features for Automatic Classification of Alzheimer’s Disease in Magnetic Resonance Images

Rueda

Arévalo

Cruz

et al. 2012

Lecture Notes in Computer Science

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The goal of this paper is to evaluate the suitability of a bag-of-feature representation for automatic classification of Alzheimer's disease brain magnetic resonance (MR) images. The evaluated method uses a bag-of-features (BOF) to represent the MR images, which are then fed to a support vector machine, which has been trained to distinguish between normal control and Alzheimer's disease. The method was applied to a set of images from the OASIS data set. An exhaustive exploration of different BOF parameters was performed, i.e. feature extraction, dictionary construction and classification model. The experimental results show that the evaluated method reaches competitive performance in terms of accuracy, sensibility and specificity. In particular, the method based on a BOF representation outperforms the best published result in this data set improving the equal error classification rate in about 10% (0.80 to 0.95 for Group 1 and 0.71 to 0.81 for Group 2).

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Micro‐structural tissue analysis for automatic histopathological image annotation

Cited by 20 publications

References 52 publications

Bag-of-visual-ngrams for histopathology image classification

Bag-of-visual-ngrams for histopathology image classification

Classification of Prostate Histopathology Images Based on Multifractal Analysis

Bag of Features for Automatic Classification of Alzheimer’s Disease in Magnetic Resonance Images

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