Method for counting mitoses by image processing in feulgen stained breast cancer sections

Kate, T.K. ten; Beliën, Jeroen A.M.; Smeulders, A.W.M.; Baak, Jan P. A.

doi:10.1002/cyto.990140302

Cited by 46 publications

(11 citation statements)

References 26 publications

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“…The images were linearly corrected for shading with two empty images, one illuminated and one dark current image. 43 The corrected grey values thus provided a measure for the local optical density.…”

Section: Image Processingmentioning

confidence: 99%

Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer.

Beliën¹,

Somi²,

Jong³

et al. 1999

J Clin Pathol

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Background-Manual counting of microvessels is subjective and may lead to unacceptable interobserver variability, which may explain conflicting results. Aims-To develop and test an automated method for microvessel counting and objective selection of the hot spot, based on image processing of whole sections, and to compare this with manual selection of a hot spot and counting of microvessels. Methods-Microvessels were stained by CD31 immunohistochemistry in 10 cases of invasive breast cancer. The number of microvessels was counted manually in a subjectively selected hot spot, and also in the same complete tumour sections by interactive and automated image processing methods. An algorithm identified the hot spots from microvessel maps of the whole tumour section. Results-No significant diVerence in manual microvessel counts was found between two observers within the same hot spot, and counts were significantly correlated. However, when the hot spot was reselected, significantly diVerent results were found between repeated counts by the same observer. Counting all microvessels manually within the entire tumour section resulted in significantly diVerent hot spots than manual counts in selected hot spots by the same observer. Within the entire tumour section no significant differences were found between the hot spots of the manual and automated methods using an automated microscope. The hot spot was found using an eight connective path search algorithm, was located at or near the border of the tumour, and (depending on the size of the hot spot) did not always contain the field with the largest number of microvessels. Conclusions-The automated counting of microvessels is preferable to the manual method because of the reduction in measurement time when the complete tumour is scanned, the greater accuracy and objectivity of hot spot selection, and the possibility of visual inspection and relocation of each measurement field afterwards. (J Clin Pathol 1999;52:184-192)

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Section: Image Processingmentioning

confidence: 99%

Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer.

Beliën¹,

Somi²,

Jong³

et al. 1999

J Clin Pathol

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“…Earliest attempts in mitotic cell counting were done by Kate et al [4] and Belien et al [5]. Kate et al have reported In the opinion of the authors of this paper, accuracy cannot be cosidered an apt performance measure as the classification of all the cells as non-mitotic alone will give an accuracy of above 90%.…”

Section: Introductionmentioning

confidence: 92%

2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery

Tripathi

Mathur

Daga

et al. 2013

2013 IEEE International Conference on Image Processing

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In this paper, we propose a 2-Sieve model for the detection of mitosis in breast cancer multispectral images. Multiresolution wavelet features & Gray Level Entropy Matrix (GLEM) features have been computed for each candidate on all the spectral bands. A novel dimensionality selection algorithm has been introduced and its performance compared with other existing algorithms. Data imbalance and data cleaning have been taken care of using classical data mining techniques. Furthermore, a Second Sieve classification is performed to increase the Positive Predictive Value (PPV) with minimal loss in Sensitivity. A final Sensitivity and PPV of 82.35% & 73.04% respectively was achieved over the testing set using the proposed scheme.

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“…In spite of great advances in histopathological image analysis by professional pathologists, there are many researches and studies for the establishment of computer-aided diagnosis (CAD) utilities to avoid dispersion in inter- and intra-pathological observations. [678910111213]…”

Section: Introductionmentioning

confidence: 99%

A Novel CAD System for Mitosis detection Using Histopathology Slide Images

et al. 2014

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Histopathology slides are one of the most applicable resources for pathology studies. As observation of these kinds of slides even by skillful pathologists is a tedious and time-consuming activity, computerizing this procedure aids the experts to have faster analysis with more case studies per day. In this paper, an automatic mitosis detection system (AMDS) for breast cancer histopathological slide images is proposed. In the proposed AMDS, the general phases of an automatic image based analyzer are considered and in each phase, some special innovations are employed. In the pre-processing step to segment the input digital histopathology images more precisely, 2D anisotropic diffusion filters are applied to them. In the training segmentation phase, the histopathological slide images are segmented based on RGB contents of their pixels using maximum likelihood estimation. Then, the mitosis and non-mitosis candidates are processed and hence that their completed local binary patterns are extracted object-wise. For the classification phase, two subsequently non-linear support vector machine classifiers are trained pixel-wise and object-wise, respectively. For the evaluation of the proposed AMDS, some object and region based measures are employed. Having computed the evaluation criteria, our proposed method performs more efficient according to f-measure metric (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) than the methods proposed by other participants at Mitos-ICPR2012 contest in breast cancer histopathological images. The experimental results show the higher performance of the proposed AMDS compared with other competitive systems proposed in Mitos-ICPR2012 contest.

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Method for counting mitoses by image processing in feulgen stained breast cancer sections

Cited by 46 publications

References 26 publications

Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer.

Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer.

2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery

A Novel CAD System for Mitosis detection Using Histopathology Slide Images

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