Classification of Thyroid Carcinoma in FNAB Cytological Microscopic Images

Gupta, B. R.; Gopinath, B.

doi:10.4018/jhisi.2010040107

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…approximation. The process is repeated on the LL1 channel and the two-level wavelet decomposition is constructed (8,9). The statistical features mean, standard deviation, entropy, variance, energy, homogeneity, contrast and correlation of the sub-bands of two-level decomposed images are calculated from thyroid FNAB images and stored in feature library as,…”

Section: Two-level Wavelet Decompositionmentioning

confidence: 99%

“…In this work, classification was performed based on the statistical feature vectors using Elman neural network (ENN), support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers (7). Gopinath et al tested majority voting based classification of papillary and medullary thyroid carcinomas using FNAB cytological images and SVM based approach in the diagnosis of thyroid malignancy using statistical texture features (8,9,13).…”

Section: Introductionmentioning

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: Two-level Wavelet Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

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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show abstract

“…The diagnostic accuracy depends on the efficient segmentation methodology and can be improved, if the segmentation of the regions of interest is clearly defined (Gopinath and Gupta, 2010a). In the current study, mathematical morphology image segmentation method is used to segment the required benign and malignant cell regions of thyroid nodules in multi-stained FNAC images.…”

Section: Image Segmentationmentioning

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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Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images

Gopinath¹,

Shanthi²

2013

Australas Phys Eng Sci Med

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An automated computer-aided diagnosis system is developed to classify benign and malignant thyroid nodules using multi-stained fine needle aspiration biopsy (FNAB) cytological images. In the first phase, the image segmentation is performed to remove the background staining information and retain the appropriate foreground cell objects in cytological images using mathematical morphology and watershed transform segmentation methods. Subsequently, statistical features are extracted using two-level discrete wavelet transform (DWT) decomposition, gray level co-occurrence matrix (GLCM) and Gabor filter based methods. The classifiers k-nearest neighbor (k-NN), Elman neural network (ENN) and support vector machine (SVM) are tested for classifying benign and malignant thyroid nodules. The combination of watershed segmentation, GLCM features and k-NN classifier results a lowest diagnostic accuracy of 60 %. The highest diagnostic accuracy of 93.33 % is achieved by ENN classifier trained with the statistical features extracted by Gabor filter bank from the images segmented by morphology and watershed transform segmentation methods. It is also observed that SVM classifier results its highest diagnostic accuracy of 90 % for DWT and Gabor filter based features along with morphology and watershed transform segmentation methods. The experimental results suggest that the developed system with multi-stained thyroid FNAB images would be useful for identifying thyroid cancer irrespective of staining protocol used.

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Classification of Thyroid Carcinoma in FNAB Cytological Microscopic Images

Cited by 7 publications

References 6 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

Computer-aided diagnosis system for classifying benign and malignant thyroid nodules in multi-stained FNAB cytological images

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