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This paper presents a genetic algorithm (GA) for combining representations of learned priors such as shape, regional properties and relative location of organs into a single framework in order to perform automated segmentation of the prostate. Prostate segmentation is typically performed manually by an expert physician and is used to determine the locations for radioactive seed placement during radiotherapy...
Traditional gamma camera collimators have been based on a standard parallel design, usually with parallel holes. The collimator determines the resolution of the camera, but is also the determining factor of the number of counts detected. The uncertainty about the origin of the detected photons is modelled by a Point Spread Function (PSF), which, in literature, is normally assumed to be a Gaussian...
The quality of the lung nodule models determines the success of lung nodule detection. This paper describes aspects of our data-driven approach for modeling lung nodules using the texture and shape properties of real nodules to form an average model template per nodule type. The ELCAP low dose CT (LDCT) scans database is used to create the required statistics for the models based on modern computer...
Recently, multi detector row computed tomography (MDCT) has been introduced into medical fields. By the development of MDCT, images with high quality are provided into medical fields. So many related image processing techniques are proposed into medical image processing fields for extraction of abnormal area. In the medical image processing field, segmentation is one of the most important problems...
Regularization methods are used in microwave image reconstruction problems, which are ill-posed. Traditional regularization methods are usually problem-independent and do not take advantage of a priori information specific to any particular imaging application. In this paper, a novel problem-dependent regularization approach is introduced for the application of breast imaging. A real genetic algorithm...
A new algorithm by using geometric active contour model with the fusion of shape and texture priors to manual segment medical images has been presented in this paper. Then the prior knowledge is merged into active contour model with its contour evolution which is evolved using a genetic algorithm technique. The new method has some advantages over classical level set methods in case of images with...
A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which...
X-ray image segmentation is an important issue in medical image analysis. Due to inconsistent X-ray absorption, the intensities are usually unevenly distributed and noisy in the processed organ, thus the object segmentation becomes difficult. In this paper we propose a new segmentation method for patella from the lateral knee X-ray images based on the active shape model (ASM). At first, a patella...
In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier descriptors introduce a large number of...
This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on...
An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses/lesions appearing in breast in different groups of benignancy/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing...
Accurate extraction of contour information from neural stem cells is important, as the shapes of neural stem cells may contain significant features for studying their activities in time lapse image sequences especially during their cleavage. In this paper, an algorithm is presented for this purpose. Fuzzy threshold method based on Zadeh's maximum entropy is adopted for cell segmentation. The best...
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