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Issac Niwas, Swamidoss
Publications (5 of 5) Show all publications
Issac Niwas, S., Palanisamy, P., Sujathan, K. & Bengtsson, E. (2013). Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets. Signal Processing, 93(10), 2828-2837
Open this publication in new window or tab >>Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets
2013 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 93, no 10, p. 2828-2837Article in journal (Refereed) Published
Abstract [en]

Breast cancer is the most frequent cause of cancer induced death among women in the world. Diagnosis of this cancer can be done through radiological, surgical, and pathological assessments of breast tissue samples. A common test for detection of this cancer involves visual microscopic inspection of Fine Needle Aspiration Cytology (FNAC) samples of breast tissue. The result of analysis on this sample by a cytopathologist is crucial for the breast cancer patient. For the assessment of malignancy, the chromatin texture patterns of the cell nuclei are essential. Wavelet transforms have been shown to be good tools for extracting information about texture. In this paper, it has been investigated whether complex wavelets can provide better performance than the more common real valued wavelet transform. The features extracted through the wavelets are used as input to a k-nn classifier. The correct classification results are obtained as 93.9% for the complex wavelets and 70.3% for the real wavelets.

National Category
Medical Image Processing
Research subject
Cell Research; Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-185094 (URN)10.1016/j.sigpro.2012.06.029 (DOI)000321599400005 ()
Available from: 2012-07-13 Created: 2012-11-20 Last updated: 2017-12-07Bibliographically approved
Issac Niwas, S., Kårsnäs, A., Uhlmann, V., Ponnusamy, P., Kampf, C., Simonsson, M., . . . Strand, R. (2013). Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas. Journal of Pathology Informatics, 4(14)
Open this publication in new window or tab >>Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
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2013 (English)In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 4, no 14Article in journal (Refereed) Published
Abstract [en]

Background:

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

Materials and Methods:

The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

Results:

We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

Conclusions:

Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

National Category
Medical Image Processing
Research subject
Behavioural Neuroscience; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-212564 (URN)10.4103/2153-3539.109881 (DOI)
Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2017-12-06Bibliographically approved
Issac Niwas, S., Palanisamy, P. & Bengtsson, E. (2013). Color deconvolution method for breast tissue core biopsy images cell nuclei detection and analysis using multiresolution techniques. International Journal of Imaging and Robotics, 9(1), 61-72
Open this publication in new window or tab >>Color deconvolution method for breast tissue core biopsy images cell nuclei detection and analysis using multiresolution techniques
2013 (English)In: International Journal of Imaging and Robotics, ISSN 2231-525X, Vol. 9, no 1, p. 61-72Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-185096 (URN)
Available from: 2012-11-20 Created: 2012-11-20 Last updated: 2017-02-08Bibliographically approved
Issac Niwas, S., Palanisamy, P. & Bengtsson, E. (2012). An Investigation on Nuclei of Histopathological Images using Curvelet Statistical Features. Paper presented at SSBA'12, Symposium on Image Analysis. KTH-Stockholm
Open this publication in new window or tab >>An Investigation on Nuclei of Histopathological Images using Curvelet Statistical Features
2012 (English)Conference paper, Oral presentation only (Other academic)
Place, publisher, year, edition, pages
KTH-Stockholm: , 2012
National Category
Medical Image Processing
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-185092 (URN)
Conference
SSBA'12, Symposium on Image Analysis
Available from: 2012-11-20 Created: 2012-11-20 Last updated: 2017-02-08
Issac Niwas, S., Kårsnäs, A., Uhlmann, V., Palanisamy, P., Kampf, C., Simonsson, M., . . . Strand, R. (2012). Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas. In: Histopathology Image Analysis (HIMA): a MICCAI 2012 workshop. Paper presented at Histopathology Image Analysis (HIMA), a MICCAI 2012 workshop, 5 October, 2012, Nice, France.
Open this publication in new window or tab >>Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas
Show others...
2012 (English)In: Histopathology Image Analysis (HIMA): a MICCAI 2012 workshop, 2012Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Background:

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/ ). It contains a large number of histological images of sections from human tissue. Tissue micro arrays are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

Methods and Material:

The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features and WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (SVM and LDA classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

Results:

Good results have been obtained by using the combinations of GLCM and wavelets and texture features, edge features, histograms, transforms, etc. (WND-CHARM). The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

Conclusions:

Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumour grading.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-188447 (URN)
Conference
Histopathology Image Analysis (HIMA), a MICCAI 2012 workshop, 5 October, 2012, Nice, France
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2013-07-05Bibliographically approved
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