Digital Color

Fernández-Maloigne, Christine; Robert-Inacio, Frédérique; Macaire, Ludovic

doi:10.1002/9781118562680

Cited by 12 publications

(11 citation statements)

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“…Minima are the core of the fuzzy region and have therefore maximal value of membership to this region. Then degrees are provided by (2), minimizing the cost C on a path. This computation is based on a criterion that includes spatial nearness and our weighted J-image magnitude.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy Color Image Segmentation using Watershed Transform

Richard¹,

Fernández-Maloigne²,

Jur³

et al. 2013

JOIG

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Abstract-In this paper, we present a segmentation technique which joins a multi-scale texture based approach and a fuzzy segmentation method using the color gradient of Di Zenzo. Thus maps of homogeneous texture patterns are enhanced by edge localization. A topographic distance is used to calculate membership degrees to each region. Both lead to segmentation using a step of watershed flooding, with few parameters to set. Moreover computation complexity has been reduced to allow treatments of images in full size and colors. Index Terms-color image segmentation, fuzzy image segmentation, color watershed, color gradient, multi-scale texture segmentation

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Section: Resultsmentioning

confidence: 99%

Fuzzy Color Image Segmentation using Watershed Transform

Richard¹,

Fernández-Maloigne²,

Jur³

et al. 2013

JOIG

Self Cite

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“…Various methods have been reported for preprocessing. Modified white patch algorithm uses a threshold for light areas of the image and corrected using grey world algorithm which enhances the image (Fernandez-Maloine et al, 2012). This uses a threshold of pixel intensity 80 for light areas of image which improves the image, as shown in Figure 2(B).…”

Section: Proposed Methodologymentioning

confidence: 99%

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

Tewary

Arun

Ahmed

et al. 2020

Journal of Microscopy

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Summary Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2‐stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC‐Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2‐stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC‐Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring. Lay description In cancer prognosis amongst the patient of breast cancer, human epidermal growth factor receptor 2 (HER2) is used as Immunohistochemical (IHC) biomarker. The correct assessment of HER2 leads to the therapeutic decision making. In regular practice, the stained tissue sample is observed under a bright microscope and the expert pathologists score the sample as negative (0/1+), equivocal (2+) and positive (3+) case. The scoring is based on the standard guidelines relating the complete and broken cell membrane as well as intensity of staining in the membrane boundary. Such evaluation is time consuming, tedious and quite often results in interobserver variability. To assist in rapid HER2 cell membrane assessment, the proposed study aims at developing an automated IHC scoring system, termed as AutoIHC‐Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. The input image is preprocessed using modified white patch and CMYK and RGB colour space were used in extracting the haematoxylin (negatively stained cells) and diaminobenzidine (DAB) stain observed in the tumour cell membrane. Segmentation and postprocessing are applied to create the masks for each of the stain channels. The membrane ma...

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“…Considering that image segmentation can be performed under various color spaces, it is essential to choose the most suitable space before describing the details of the algorithm. The spaces * * V * and * * * are commonly applied to image segmentation since the color difference is in accordance with the Euclidean distance in either feature space [28,29].…”

Section: Gdf-ncut Principlesmentioning

confidence: 99%

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

Liu¹,

Wang²,

Li³

et al. 2018

Mathematical Problems in Engineering

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Color image segmentation is fundamental in image processing and computer vision. A novel approach, GDF-Ncut, is proposed to segment color images by integrating generalized data field (GDF) and improved normalized cuts (Ncut). To start with, the hierarchy-grid structure is constructed in the color feature space of an image in an attempt to reduce the time complexity but preserve the quality of image segmentation. Then a fast hierarchy-grid clustering is performed under GDF potential estimation and therefore image pixels are merged into disjoint oversegmented but meaningful initial regions. Finally, these regions are presented as a weighted undirected graph, upon which Ncut algorithm merges homogenous initial regions to achieve final image segmentation. The use of the fast clustering improves the effectiveness of Ncut because regions-based graph is constructed instead of pixel-based graph. Meanwhile, during the processes of Ncut matrix computation, oversegmented regions are grouped into homogeneous parts for greatly ameliorating the intermediate problems from GDF and accordingly decreasing the sensitivity to noise. Experimental results on a variety of color images demonstrate that the proposed method significantly reduces the time complexity while partitioning image into meaningful and physically connected regions. The method is potentially beneficial to serve object extraction and pattern recognition.

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Digital Color

Cited by 12 publications

References 0 publications

Fuzzy Color Image Segmentation using Watershed Transform

Fuzzy Color Image Segmentation using Watershed Transform

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

A Fast Color Image Segmentation Approach Using GDF with Improved Region-Level Ncut

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