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In order to solve the problem of PCNN with improper parameter selection and determination of circulation iterations which leads to the image owe-segmentation or over-segmentation, an Iterative Self-organizing Data Clustering (ISODC) model is used in this paper to resolve the problems of the PCNN parameters selection and requiring multiple circulation. By using ISODC clustering search decision-making...
For the problem of low accuracy using K-means clustering algorithm to segment noisy brain magnetic resonance imaging (MRI) images, this paper proposed a strategy to improve segmentation accuracy. Firstly, the strategy uses wavelet transform to brain MRI image denoising, secondly, brain MRI image is segmented by k-means clustering algorithm. Experimental results show that the proposed strategy can...
Resting state fMRI (rsfMRI) has been demonstrated to be an effective modality by which to explore the functional networks of the human brain, as the low-frequency oscillations in rsfMRI time courses between spatially distant brain regions show the evidence of correlated activity patterns in the brain. This paper proposes a novel surface-based data-driven framework to explore these networks through...
Thresholding is an easy yet efficient method in image segmentation, when dividing different objects with distinct gray-levels. Its main problem is how effective the thresholds divide the image. A new multilevel thresholding method is proposed in this study, which bases on voting response of all histogram bins to each bin. Smoothed the histogram, the method accumulates all voting of other bins by a...
Fingerprint images are textural images consisting of ridges and valleys. The orientation of textures can be determined by orientation field computation. Fingerprint orientation field is the critical basis for fingerprint image segmentation, filtering enhancement and matching processes, and the fingerprint orientation field algorithm plays a very important role in the applied Automated Fingerprint...
The human brain anatomy is extremely variable across individuals in terms of its size, shape, and structure patterning. In this paper, a novel method is proposed for grouping brain MR images into different patterns. This method adopts the affinity propagation methodology to partition a population of brain images into different clusters. In the affinity propagation method, the tissue-segmented and...
Studying the growth/recurrence of glioblastoma multiforme (GBM) is very important not only for diagnosis but also for understanding and detecting the recurrence of GBM after surgery. In this paper, a novel DTI-based method is proposed to analyze the recurrence pattern of GBM based on serial magnetic resonance imaging (MRI). After detecting the tumor shapes from T1-weighted images, the diffusion pattern...
In order to get moving object in video frames, a segmentation method is proposed. The phase-based method is devised for detection of moving object boundary area in the video frame. This boundary detection scheme is based on the characteristics of a phase-matched difference image frame, and based on the assessment of the phase-matched difference between image frames. It is shown to be sensitive to...
A self-enhanced SVM (support vector machines) building detection scheme is discussed. The scheme was designed for 1-metre resolution satellite imagery analysis. The scheme is a learning based segmentation without any prior prepared training data set. In the initial stage, an adaptive two-dimension Otsu algorithm is adopted to segment the image primarily into buildings and non-buildings. Then the segmented...
In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. As a new kind of machine learning, support vector machine (SVM) based on statistical learning theory (SLT) has high generalization ability, especially for dataset with small number of samples in high dimensional space. SVM was originally developed...
In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. Support vector machine (SVM) has high generalization ability, especially for dataset with small number of samples in high dimensional space. However, selecting parameters for SVM is a complicated problem which directly affects segmentation result...
Curve evolution based level set has been widely used in medical image segmentation. However, the high computational cost excludes its use in robust real-time medical image segmentation. This paper presents a novel segmentation method based on improved narrow band method (INBM). Firstly, images are transformed from Cartesian space to log-polar coordinate space. The space invariant theory of human vision...
This paper presents a new image segmentation method - FWTN (first watershed then normalized cut) based on watersheds and graph theory to solve the over-segmentation problem of watersheds. FWTN firstly uses normalized cut to segment between regions after applying watersheds, and then generates the final segmented images. The algorithm can successfully solve over-segmentation problem, and at the same...
Registration of DTI data with structure data, such as SPGR data, has import application in quantitative analysis of brain microstructures such as tissue diffusivity. However, due to potential problems such as EPI geometric distortion, partial volume effect and image reslicing errors, accurate registration of these two types of MRI images is challenging. In this paper, we present a novel deformable...
The development of automated and robust computational algorithms for 3D cell segmentation remains challenging in situations where the cells are touching each other or connected together. In this paper, we present a novel automated method that aims to tackle the aforementioned challenges in the segmentation of clustered or connected 3D cells. We first diffuse the gradient vector field with an elastic...
Proposed a novel image segmentation method based on Markov random field (MRF) and context information. The method introduces the relationships of observed image intensities and distance between pixels to the traditional neighborhood potential function, so that to describe the probability of pixels being classified into one class. We transform the segmentation process to maximum a posteriori (MAP)...
We present a method for tissue classification based on diffusion-weighted imaging (DWI)/diffusion tensor imaging (DTI) data. Our motivation is that independent tissue segmentation based on DWI/DTI images provides complementary information to the tissue segmentation result using structural MRI data alone. The basis idea is to classify the brain into two compartments by utilizing the tissue contrast...
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