Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images

Hrebień, Maciej; Stec, Piotr; Nieczkowski, Tomasz; Obuchowicz, Andrzej

doi:10.2478/v10006-008-0015-x

Cited by 41 publications

(13 citation statements)

References 19 publications

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“…Because of the malignant characteristics of cancer cells are contained in the cell nucleus, the isolation of the cell nucleus is an important task of segmentation. In the past years, many methods for the segmentation of cell regions in microscopic biomedical images have been presented (15)(16)(17). The methods most commonly used for image segmentation can be categorized into two classes, namely region-based and boundary-based approaches (18).…”

Section: Cell Segmentationmentioning

confidence: 99%

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Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Gopinath¹,

Shanthi²

2014

Technol Cancer Res Treat

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An automated medical diagnosis system has been developed to discriminate benign and malignant thyroid nodules in multi-stained fine needle aspiration biopsy (FNAB) images using multiple classifier fusion and presented in this paper. First, thyroid cell regions are extracted from the auto-cropped sub-image by implementing mathematical morphology segmentation method. Subsequently, statistical features are extracted by two-level wavelet decomposition based on texture characteristics of the thyroid cells. After that, decision tree (DT), k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) classifiers are used separately to classify thyroid nodules into benign and malignant.The four individual classifier outputs are then fused together using majority voting rule and linear combination rules to improve the performance of the diagnostic system. The classification results of ENN and SVM classifiers show an overall diagnostic accuracy (DA) of 90%, sensitivity (Se) of 85% and 100%, specificity (Sp) of 90% and 90% respectively. However, the best diagnostic accuracy of 96.66% is obtained by multiple classifier fusion with majority voting rule and linear combination rules. The experimental results show that the proposed method is a useful tool for the diagnosis of thyroid cancer and can provide a second opinion for a physician's decision.

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Section: Cell Segmentationmentioning

confidence: 99%

“…i j , [16] where, f(i,j) is the gray-level value for each pixel in the region of interest, N is the total number of pixels in the region of interest and µ i , µ j , σ i , σ j, σ j 2 are means and standard deviations of f (i,j).…”

Section: Two-level Wavelet Decompositionmentioning

confidence: 99%

Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Gopinath¹,

Shanthi²

2014

Technol Cancer Res Treat

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“…It is used for extracting shape and size information from the images (Hrebien et al, 2008;Kimori, 2010;2011;Gopinath and Shanthi, 2012). A morphology based image segmentation method is presented in Figure 2.…”

Section: Mathematical Morphologymentioning

confidence: 99%

Support Vector Machine Based Diagnostic System for Thyroid Cancer using Statistical Texture Features

Gopinath¹,

Shanthi²

2013

Asian Pacific Journal of Cancer Prevention

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“…Hrebień, P. Steć, T. Nieczkowski and A. Obuchowicz also focus on image pre-processing for image analysis of cytological preparations [11]. At the same time, emphasis is placed on the fact that such processing is important for any methods of image segmentation.…”

Section: Images Of Cytological Preparations As An Object Of Researchmentioning

confidence: 99%

Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>

Lyashenko¹,

Babker²

2017

OJBD

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Processing and analyzing of medical images is one of the priority research areas. At the same time, the processing of images of cells occupies a special place. This is due to the fact that such studies allow for a comprehensive diagnosis of the state of human health, identify and prevent the development of diseases in the early stages. We investigate the effectiveness of using wavelet analysis in color models, taking into account the preliminary change in the contrast of the input image. We consider the HSV color model and the image contrast modification procedure, which is based on the histogram change in the specified range with gamma correction. As a criterion for choosing parameters for changing the contrast of the image, we consider the entropy of the image. We also showed the advisability of using the value of the entropy index for the subsequent improvement of image analysis based on the wavelet ideology. We examined the general sequence of action for the analysis of image of megaloblastic anemia cells. This sequence is based on the choice of parameters for changing the contrast of the image and application of wavelet ideology.

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Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images

Cited by 41 publications

References 19 publications

Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Support Vector Machine Based Diagnostic System for Thyroid Cancer using Statistical Texture Features

Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>

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Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images

Cited by 41 publications

References 19 publications

Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Development of an Automated Medical Diagnosis System for Classifying Thyroid Tumor Cells using Multiple Classifier Fusion

Support Vector Machine Based Diagnostic System for Thyroid Cancer using Statistical Texture Features

Using of Color Model and Contrast Variation in Wavelet Ideology for Study &lt;i&gt;Megaloblastic Anemia Cells&lt;/i&gt;

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Using of Color Model and Contrast Variation in Wavelet Ideology for Study <i>Megaloblastic Anemia Cells</i>