Computerized Scene Segmentation for the Discrimination of Architectural Features in Ductal Proliferative Lesions of the Breast

Anderson, Neil; Hamilton, Peter; Bartels, Peter H.; Thompson, Deborah; Montironi, Rodolfo; Sloan, James M.

doi:10.1002/(sici)1096-9896(199704)181:4<374::aid-path795>3.0.co;2-n

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…The ability to analyse the quantitative characteristics of individual objects and selectively process them using methods best suited to their individual features is vital to the identification of the different components found in histopathological images. This has been illustrated previously by our group for the analysis of colonic, breast, and prostate histology32–34. In this study, we applied the same principles to the segmentation of nuclei in cervical squamous epithelium.…”

Section: Discussionmentioning

confidence: 91%

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

et al. 2000

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The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n=30), koilocytosis (n=46), CIN 1 (n=52), CIN 2 (n=56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology.

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Section: Discussionmentioning

confidence: 91%

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

et al. 2000

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“…8,9 Classified breast data can also be of use in dose estimation and computer-aided diagnosis. Although various approaches have been investigated to classify breast tissue [13][14][15][16] and to identify microcalcifications and tumors, [17][18][19][20] accurate breast tissue classification remains a challenge. Several groups have investigated histogram-based classification methods in order to separate breast tissue into three types of tissues, i.e., skin, fat, and glandular tissue.…”

Section: Introductionmentioning

confidence: 99%

Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Yang

Sechopoulos

et al. 2012

Medical Physics

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“…While the use of multispectral light microscopy is new to cyto/histopathology, many researchers have used single or dual narrow-band filters to enhance imagery for particular stains, most using a red filter (or the red channel of an RGB image) for enhancement of Hematoxylin or Feulgen staining [8-12], and some using a green filter for enhancement of Feulgen staining [13-16]. …”

Section: Introductionmentioning

confidence: 99%

Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

Boucheron

Harvey

et al. 2007

BMC Cell Biol

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Background : We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420-700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis.

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Computerized Scene Segmentation for the Discrimination of Architectural Features in Ductal Proliferative Lesions of the Breast

Cited by 22 publications

References 7 publications

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

Cupping artifact correction and automated classification for high‐resolution dedicated breast CT images

Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

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