Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Boucheron, Laura E.; Manjunath, B.S.; Harvey, Neal R.

doi:10.1109/icassp.2010.5495124

Cited by 23 publications

(21 citation statements)

References 13 publications

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“…Boucheren et. al [10] achieved good classification results on breast cancer histopathology with the imperfectly segmented nuclei. In addition to glands and nuclei, the malignancy drastically changes the density of lumina and stroma in tissue.…”

Section: Introductionmentioning

confidence: 99%

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

Kalkan

Nap

Duin

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. In this study, we propose a computational diagnosis system for detecting the colorectal cancer from histopathological slices.The computational analysis was usually performed on patch level where only a small part of the slice is covered. However, slice-based classification is more realistic for histopathological diagnosis. The developed method combines both textural and structural features from patch images and proposes a two level classification scheme. In the first level, the patches in slices are classified into possible classes (adenomatous, inflamed, cancer and normal) and the distribution of the patches into these classes is considered as the information representing the slices. Then the slices are classified using a logistic linear classifier. In patch level, we obtain the correct classification accuracies of 94.36% and 96.34% for the cancer and normal classes, respectively. However, in slice level, the accuracies of the 79.17% and 92.68% are achieved for cancer and normal classes, respectively.

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Section: Introductionmentioning

confidence: 99%

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

Kalkan

Nap

Duin

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…As the Gleason grade increases, epithelial cells randomly duplicate, disturbing the normal structure of glands. 4 The higher grade cells are described by irregular morphology in nuclei, larger nuclei, and less cytoplasm than lower grades as shown in Fig. 2.…”

Section: Features Extracted From Shearlet Transformmentioning

confidence: 99%

“…Therefore, most of automated histological analysis methods first segment the cell nuclei, then extract features from cell nuclei and use them for classification. [4][5][6] For example, Boucheron et al 4 performed image segmentation on histopathology images of breast and used the extracted features for breast cancer detection. Farjam et al 5 segmented the prostate glands and extracted structural features from them and used them in a tree-structured algorithm for automatic Gleason grading of prostate.…”

Section: Introductionmentioning

confidence: 99%

Microscopic medical image classification framework via deep learning and shearlet transform

2016

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Abstract. Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computeraided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the magnitude and phase of shearlet coefficients. Then we feed shearlet features along with the original images to our CNN consisting of multiple layers of convolution, max pooling, and fully connected layers. Our experiments show that using the magnitude and phase of shearlet coefficients as extra information to the network can improve the accuracy of detection and generalize better compared to the state-of-the-art methods that rely on hand-crafted features. This study expands the application of deep neural networks into the field of medical image analysis, which is a difficult domain considering the limited medical data available for such analysis.

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“…The second dataset contains malignant and normal breast cancer tissue dataset [159] and the third contains colon cancer tissues [160]. For all the three datasets, we show that including the saliency improves the classification accuracy and also performs significantly better than the state of art methods.…”

Section: Discussionmentioning

confidence: 91%

“…For example, in differentiation between malignant and benign tumor, often the smoothness of the nuclei are taken into account [159]. While in other applications there different types to tissues are required to be classified [160] based on their textural patterns to determine cellular composition.For example, in Fig.…”

Section: Application To Tissue Image Classificationmentioning

confidence: 99%

Dictionary Learning and Sparse Representation for Image Analysis with Application to Segmentation, Classification and Event Detection

Sarkar

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Sparse representation based dictionary learning has been exploited in solving various image analysis problems -image classification, tracking, quality assessment, de-noising, image reconstruction. The objective of dictionary learning is to obtain an adaptive basis function from the data and simultaneously provide a compact representation. In this work we employ sparse representation based dictionary learning techniques for segmentation, image classification and video analysis problems.In image and video processing applications, one of the major challenges is the choice of appropriate features for image representation. Various techniques exist that employ different analytical methods to extract color, texture and frequency information from images. However, these methods do not identify which of this information are more relevant for a particular image. Neither do these methods have any discrimination power to recognize more informative local image regions.In this work, we first tackle the problem of query specific image feature descriptor selection.Depending on the image content, different features e.g., color texture, structure can prove to be more relevant in representing and discriminating an image. We use a discriminative dictionary learning method in designing a classifier and an information theoretic measure to select the most appropriate feature for an image. This method attempt to identify the feature descriptors that provide more information about an image conditioned on the available images in a class.In image classification, while identifying the relevant feature type is important, it is also iii Abstract crucial to identify the essential contents of an image which discriminate it from the others.While the above mentioned solution is appropriate for determining image specific feature type, it does not incorporate any local image analysis to identify image regions associated with an object. To address this problem, we develop a method that leverages salient object detection framework to learn the dictionary and sparse codes from an image. The method simultaneously detects relevant image regions and computes a compact image representation.We also devise similarity measures exploiting the sparse representations for comparing image pairs. This similarity measure is used in image classification particularly for scenarios where training data is limited. Our method outperformed the state of the art methods by an average of 12% in overall accuracy for histo-pathological tissue image classification Although the above mentioned saliency guided dictionary learning method is applied to image classification, the application is not limited to just object recognition. The method is hence exploited for event detection from video. The saliency based dictionary learning and the similarity measure is used first for a frame by frame analysis to identify the temporal occurrence of an event. To make the system more robust to occlusion, dynamic background, we further employ a spatio-temporal saliency driven low rank ...

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Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer

Cited by 23 publications

References 13 publications

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

Automated Colorectal Cancer Diagnosis for Whole-Slice Histopathology

Microscopic medical image classification framework via deep learning and shearlet transform

Dictionary Learning and Sparse Representation for Image Analysis with Application to Segmentation, Classification and Event Detection

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