Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method

Belsare, Amoli D.; Mushrif, Milind M.; Pangarkar, M. A.; Meshram, Nirvedh H.

doi:10.1111/jmi.12361

Cited by 23 publications

(9 citation statements)

References 41 publications

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“…From the extensive literature review, it is obvious that the assessment for automated segmentation and classification of IC nuclei has not been tried till date to the best of our understanding (Saha et al ., ). Most of the researchers reported nuclei segmentation methodology using fine needle aspiration cytology (FNAC) and histology images (Naik et al ., ; Niwas et al ., ; George et al ., ; George et al ., ; Niwas et al ., ; Belsare et al ., ; Nguyen et al ., ). Lagrange's interpolation is one of the best methods which can be used for overlapping area segmentation.…”

Section: Comparison With Other Methodsmentioning

confidence: 97%

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Saha

Arun

Agarwal

et al. 2017

Journal of Microscopy

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Imprint cytology (IC) refers to one of the most reliable, rapid and affordable techniques for breast malignancy screening; where shape variation of H&E stained nucleus is examined by the pathologists. This work aims at developing an automated and efficient segmentation algorithm by integrating Lagrange's interpolation and superpixels in order to delineate overlapped nuclei of breast cells (normal and malignant). Subsequently, a computer assisted IC tool has been designed for breast cancer (BC) screening. The proposed methodology consists of mainly three subsections: gamma correction for preprocessing, single nuclei segmentation and segmentation of overlapping nuclei. Single nuclei segmentation combines histogram-based thresholding and morphological operations; where segmentation of overlapping nuclei includes concave point detection, Lagrange's interpolation for overlapping arc area detection and the fine segmentation of overlapped arc area by superpixels. Total 16 significant features (p < 0.05) quantifying shape and texture of nucleus were extracted, and random forest (RF) classifier was skilled for automated screening. The proposed methodology has been tested on 120 IC images (approximately 12 000 nuclei); where 98% segmentation accuracy and 99% classification accuracy were achieved. Besides, performance evaluation was studied by using Jaccard's index (= 94%), correlation coefficient (= 95%), Dice similarity coefficient (= 97%) and Hausdorff distance (= 43%). The proposed approach could offer benefit to the pathologists for confirmatory BC screening with improved accuracy and could potentially lead to a better shape understanding of malignant nuclei.

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Section: Comparison With Other Methodsmentioning

confidence: 97%

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Saha

Arun

Agarwal

et al. 2017

Journal of Microscopy

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“…The studies are summarized in Table 1. In this section, methods proposed for segmenting DRAs,[31323334] epithelial nuclei,[5151617181935] lymphocyte cells,[71320] tubule,[2122232425] and mitotic figures[682627282930] are discussed.…”

Section: Classification Of the Reviewed Studiesmentioning

confidence: 99%

“…Belsare et al . [25] proposed a novel integrated spatio-color-texture-based graph partitioning method to address this issue and achieved a correct classification rate (CCR) of 92% for segmentation.…”

Section: Classification Of the Reviewed Studiesmentioning

confidence: 99%

Computer-based image analysis in breast pathology

Gandomkar

Brennan

Mello‐Thoms

2016

Journal of Pathology Informatics

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Whole slide imaging (WSI) has the potential to be utilized in telepathology, teleconsultation, quality assurance, clinical education, and digital image analysis to aid pathologists. In this paper, the potential added benefits of computer-assisted image analysis in breast pathology are reviewed and discussed. One of the major advantages of WSI systems is the possibility of doing computer-based image analysis on the digital slides. The purpose of computer-assisted analysis of breast virtual slides can be (i) segmentation of desired regions or objects such as diagnostically relevant areas, epithelial nuclei, lymphocyte cells, tubules, and mitotic figures, (ii) classification of breast slides based on breast cancer (BCa) grades, the invasive potential of tumors, or cancer subtypes, (iii) prognosis of BCa, or (iv) immunohistochemical quantification. While encouraging results have been achieved in this area, further progress is still required to make computer-based image analysis of breast virtual slides acceptable for clinical practice.

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“…Ilea et al [3] studied an image segmentation algorithm that only extract texture features, and Tashk et al [4] studied one that only extract texture features. Belsare et al [5] proposed a hyper-pixel generation method based on similarity, combined with the text representation to form the space-texture-color map of breast histology images. Xu et al [6] proposed an unsupervised SNMF algorithm, which is divided into two steps: color unmixing and spatial segmentation.…”

Section: Introductionmentioning

confidence: 99%

Large-scale tissue histopathology image segmentation based on feature pyramid

Qin

Chen

Zeng

et al. 2018

J Image Video Proc.

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Histopathology image analysis is a gold standard for cancer recognition and diagnosis. But typical problems with histopathology images that hamper automatic analysis include complex clinical features, insufficient training data, and large size of a single image (always up to gigapixels). In this paper, an image semantic segmentation algorithm based on feature Pyramid (ResNet50-GICN-GPP) is proposed. Firstly, the patch sampling method is adopted to resample the image, reduce the size of a single sample, and expand the number of training samples. Secondly, design the whole convolution network based on ResNet50 learning feature location information, then use GICN structure and deconvolution network to integrate multi-level features. Finally, in order to solve the problem that the GICN structure may lose the small object, the GPP structure should be joined to explore the multi-scale semantic information. The proposed method achieves 63% of the average segmentation accuracy (Dice coefficient) on Camelyon16 and Gastric WSIs Data, compared with U-Net, FCN and SegNet which has 10~20% improvement, and fully demonstrates the effectiveness of this method in different types of cancer. By experimentally comparing the segmentation accuracy of various scales of cancerous tissues, the performance of ResNet50-GICN-GPP is balanced and the multi-scale information localization is more accurate.

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Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method

Cited by 23 publications

References 41 publications

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Computer-based image analysis in breast pathology

Large-scale tissue histopathology image segmentation based on feature pyramid

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