Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles

Zhang, Yungang; Zhang, Bailing; Coenen, Frans; Lu, Wenjin

doi:10.1007/s00138-012-0459-8

Cited by 84 publications

(44 citation statements)

References 35 publications

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“…Thus, we use completed local binary patterns (CLBPs) [49] for extracting local textural features, gray level co-occurrence matrix (GLCM) [50] statistics for representing global textures, and the curvelet transform [51] for shape description. These feature descriptors have shown promising results in our previous work on biopsy image classification [52].…”

Section: Breast Cancer Biopsy Image Setmentioning

confidence: 87%

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

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Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

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Section: Breast Cancer Biopsy Image Setmentioning

confidence: 87%

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

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“…Based on which shape descriptors are most distinguishing during classification, the authors can identify which shapes are indicative of each tissue type. In [53], hand-selected ROIs of breast tissue are classified as normal, in situ cancer, or invasive cancer. They use generic features including local binary patterns, cooccurence matrix statistics, and curvelet coefficient statistics.…”

Section: Diagnosismentioning

confidence: 99%

Automated Histology Analysis: Opportunities for signal processing

McCann

Ozolek

Castro

et al. 2015

IEEE Signal Process. Mag.

115

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“…The previous studies have used two main approaches for feature extraction. In the first approach, they extract features for each image pixel using various methods including intensity histograms [1], [2], co-occurrence matrices [3], [4], filters [5], [6], and local binary patterns [7], [8]. They then define global features accumulating the pixels' features over an entire image.…”

Section: Index Terms-automatedmentioning

confidence: 99%

Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification

Gultekin

Koyuncu

Sökmensüer³

et al. 2015

IEEE Trans. Med. Imaging

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Abstract-In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.Index Terms-Automated cancer diagnosis, blob, digital pathology, histopathological image representation, tissue decomposition model.

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Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles

Cited by 84 publications

References 35 publications

One-class kernel subspace ensemble for medical image classification

One-class kernel subspace ensemble for medical image classification

Automated Histology Analysis: Opportunities for signal processing

Two-Tier Tissue Decomposition for Histopathological Image Representation and Classification

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