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[IEEE 2018 3rd International Conference for Convergence in Technology (I2CT) - Pune (2018.4.6-2018.4.8)] 2018 3rd International Conference for Convergence in Technology (I2CT) - Modified Level-Set for Segmenting Breast Tumor from Thermal Images
摘要： Contactless, painless and radiation-free thermal imaging technique is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer aided diagnosis of breast cancer. This work uses a modified version of level-set called marker-controlled level-set for segmentation along with pre-processing. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: a) marker-controlled Level-set segmentation of anisotropic diffusion filtered preprocessed image versus b) Segmentation using marker-controlled level-set on a Gaussian-filtered image. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer aided diagnosis of breast cancer.
关键词： Breast,Thermograms,Gaussian,Anisotropic diffusion,Segment,Level set
更新于2025-09-23 15:22:29
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Bio-inspired reaction diffusion system applied to image restoration
摘要： In this paper, we propose a new model of nonlinear and anisotropic reaction diffusion system applied to image restoration and to contrast enhancement. This model is based on a system of partial differential equations of type Fitzhugh-Nagumo. In the first, we give the comparison with the previous model, then, we show the robustness and the performance of our algorithm through a number of experimental results.
关键词： reaction diffusion system,image restoration,nonlinear anisotropic diffusion,Fitzhugh-Nagumo model,mathematical biology model,contrast enhancement
更新于2025-09-09 09:28:46
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Reduction of False Microaneurysms in Retinal Fundus Images using Fuzzy C-Means Clustering in terms NLM Anisotropic Filter
摘要： The identification of MAs is an important phase in the research and grading of suffering from diabetes retinopathy. We present clustering strategy to identify the microaneurysms from the optic disk and cup in the retinal fundus pictures. Fuzzy C-Means (FCM) Clustering is used for clustering the information in which the information factors are grouped with different account level. The first and major phase is preprocessing function, in which the optic cup and disk of the feedback picture is being turned. Originally the optic hard disk is turned in some position and the range between the information factors is calculated and a group is established in accordance with the centroid. For retrieving micro aneurysms in all retinal images in our previous work we used SVM Classification filter in Fuzzy C-Means Clustering. In this paper we propose an effective filtering technique for micro aneurysms detection in retinal image preprocessing. Instead of SVM Filtering in terms of technique we used NLM Anisotropic Filter to process retinal images. Tested on the various simulated retina data repositories combining rotation and scaling, the developed method presents good results and shows robustness to rotations and scale changes.
关键词： Biomedical image processing,pattern recognition,Fuzzy C Means Clustering,Fundus Image,image classification,Anisotropic Diffusion Filter,medical decision-making,Non-Local Methodologies,Spatial Information
更新于2025-09-09 09:28:46
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Visual and Quantitative Assessment of a New Anisotropic Diffusion Filter (Statistical Transfer with Optimizing Noise and Edge Sensing) for Positron Emission Tomography
摘要： Post-filtering with a Gaussian filter is commonly used to reduce noise in positron emission tomography (PET) images. However, its non-selective smoothing obscures the edges of lesions or organs. We compared the performance of a newly developed anisotropic diffusion filter called “Statistical Transfer with Optimizing Noise and Edge Sensing” (STONES) with that of the Gaussian filter for small lesions on PET images. We selected seven PET/computed tomography (CT) image slices of the lungs from three patients with multiple lung metastases. For each slice, the lesion detection rates by two physicians (A and B) were compared for Gaussian- and STONES-filtered PET images. The maximum standardized uptake (SUVmax) values of the detected lesions were also compared for non-, Gaussian-, and STONES-filtered images. Physician A detected 19 lesions in the Gaussian-filtered images and 23 lesions in the STONES-filtered images, while Physician B detected 14 lesions in the Gaussian-filtered images and 19 lesions in the STONES-filtered images. SUVmax for the STONES-filtered images was significantly higher and closer to that of the non-filtered images compared to those for the Gaussian-filtered images. STONES improved the detection rate and increased SUVmax in comparison with Gaussian filter. Thus, it should be more advantageous for the detection of small lesions with PET.
关键词： Anisotropic diffusion filter,Lung,Edge preservation,Gaussian filter,STONES,FDG-PET
更新于2025-09-04 15:30:14
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Two stage image de-noising by SVD on large scale heterogeneous anisotropic diffused image data
摘要： De-noising of images along with the edge enhancement has always been a challenging task in large scale heterogeneous image data. This paper presents a two stage image de-noising as well as edge enhancement method where in the first stage two copies of input noisy image are created through diffusion. The first copy is got by using anisotropic diffusion method which employ optimal diffusion function while the second copy is generated to improve the sharp edges by applying the combination of inverse heat diffusion and Canny edge detector. In the next stage, the singular value decomposition is applied on the two copies achieved in first stage to reduce the noise and improve the quality of detected edges. The optimal number of significant singular values have been estimated by the analysis of signal to noise ratio of singular value decomposed images of first copy. The singular values extracted from the second copy of the diffused image are superimposed with non decreasing weights from linear weighting function. Finally the sharp edged and noise reduced output image is generated by taking the linear combination of two singular value decomposed images. The performance of the proposed method has been compared with existing methods based on singular value decomposition as well as anisotropic diffusion. The experimental results exhibit that the proposed method efficiently enhances the edges by reducing the noisy significantly.
关键词： Image de-noising,Anisotropic diffusion,Edge enhancement,Singular value decomposition
更新于2025-09-04 15:30:14