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The lack of labeled medical data is a severe challenge of applying CNNs in medical image segmentation. The common method to solve this problem is employing patches extracted from every pixel of the entire image as train samples. But classifying every pixel in the image is time-consuming, which is not appropriate in practical medical application. This paper proposed a fast segmentation algorithm based...
This paper presents a new strategy for the segmentation of brain images from the volumetric Magnetic Resonance Imaging (MRI). We propose a new segmentation technique that hybridize an evolutionary algorithm, called the Memetic Programming (MP) algorithm, with the Region Growing (RG) technique. The MP algorithm generates new threshold functions and then the RG uses these thresholds to perform an efficient...
An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
Medical image segmentation is a complex and challenging task due to the intrinsic nature of the images. The brain has particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues, in order to prescribe appropriate therapy. Recently, rough sets and fuzzy sets has proved its soundness and usefulness in many medical applications including...
In order to improve the robustness of the conventional fuzzy C-means (FCM) clustering algorithms for image segmentation, a robust information fuzzy clustering algorithm is proposed in this paper. This is an extension of the information-theoretic framework into the FCM-type algorithms. Combining these two concepts and modifying the objective function of the FCM algorithm, we are able to solve the sensitivity...
Traditional clustering methods do not take into account any relations possibly present in data. This paper introduces a contiguity-constrained algorithm with an aggregation index which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed method in the case of medical image segmentation.
Traditional watershed algorithm often causes over-segmentation because of its high sensitivity to the weak edge and the noise. To overcome this drawback and in light of the characteristics of medical image, a new segmentation algorithm based on watershed transformation and rough set theory is proposed. The original image is partitioned into the edge-detail sub-image and smooth sub-image according...
To effectively diagnose and monitor the treatment of diseases such as osteoarthritis, the segmentation, processing and analysis of mass volumes of medical images is gaining high importance. In this paper, a new fully automated content-based segmentation framework is proposed. The framework is designed to be compatible with a wide variety of segmentation techniques. To this end, a novel content-based...
In this paper, a new method that uses relative contrast is proposed for medical image segmentation problems. Generally, the absolute intensity values of different features are mapped into a comparable range with a normalization method, but the differences across patients are not considered. In order to utilize the patient-specific information from medical images, we use relative contrast between the...
Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the...
In the paper, we first discussed the method of skull-stripping based on edge detection in detail. The method can extract the brain tissue from a normal MRI (magnetic resonance image) quickly and accurately. But if there are tumors at the boundary of the brain tissues whose intensity is different with normal tissues', some unwanted edge will appear while detecting the boundary of the brain tissue....
Image segmentation is one of the mostly used procedures in the medical image processing applications. Due to the high resolution characteristic of the medical images and a large amount of computational load in mathematical methods, medical image segmentation process has an excessive computation complexity. Recently, Field Programmable Gate Array (FPGA) implementation capable of performing many complex...
The method of utilizing available prior information in the popular FCM algorithm and assesses its benefits in estimating the intensity inhomogeneities and segmenting human brain MRI volumes is studied in this paper. The intensity inhomogeneities in medical images are associated with the acquisition sequences and imperfections in the radio-frequency coils in MRI scanners. Presence of intensity inhomogeneities...
In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe...
Watershed transformation is a common technique for image segmentation. However, its use for medical image segmentation has been limited particularly due to over-segmentation. In response to the characteristics of medical image, especially the contour extraction from the MRI (magnetic resonance imaging) brain image, this paper proposes an improved method in order to overcome the drawbacks. Firstly,...
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that...
Limited spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities variation make the assignment of segmentation of medical images is greatly difficult. Mathematical algorithm supported automatic segmentation system plays an important role in segmentation of medical imaging. This paper presents an effective fuzzy segmentation algorithm for breast magnetic resonance...
In this paper we introduce a noise-resilient edge detection algorithm for brain MRI images. Also, an improved edge detection based on Canny edge detection algorithm is proposed. Computer simulations show that the proposed algorithm is resilient to impulsive noise which makes up for the disadvantages of Canny algorithm, and can detect more edges of MRI brain images effectively. Also, the concept of...
Medical image segmentation is an indispensable process in viewing and measuring various structures in the brain. However, medical images are inherently low contrast, vague boundaries, and high correlative. The traditional fuzzy c-means (FCM) clustering algorithm considers only the pixel attributes. This leads to accuracy degradation with image segmentation. To solve this problem, this paper proposes...
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