Fusion of MRI and CT brain images using histogram equalization
01 Dec 2012-pp 1-4
TL;DR: The removal of noise in the image by using adaptive filtering, the system is evaluated and the accurate output will be produced and the fusion algorithm of foreground images and background images is studied.
Abstract: Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images to retaining the important features of each image. The resulting image contains more information as compared to individual images. Multiple image fusion is an important technique used in aerial and satellite imaging, medical imaging, robot vision, digital camera application and battle field monitoring. The histogram equalization method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of functional and structural parts in MRI and CT images. Based on this, the removal of noise in the image by using adaptive filtering, the system is evaluated and the accurate output will be produced. The fusion algorithm of foreground images and background images is studied. Here, the foreground image is MRI and background image is CT image.
Citations
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TL;DR: A computer-aided pelvic framework, which leverages an ensemble preprocessing method to improve PLN segmentation and the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, and the fusion of structural and diffusion MRI and the extraction of morphological features of segmented PLNs for the final classification of PLNs as suspect or non-suspect.
Abstract: Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.
1 citations
Cites methods from "Fusion of MRI and CT brain images u..."
...Image fusion techniques have been widely applied for brain diagnosis and treatment to improve imaging and diagnostic performances such as Positron Emission Tomography (PET)-MRI [9, 10, 11, 12] and Computed Tomography (CT)-MRI [13, 14]....
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References
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TL;DR: The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model and the PCNN image decomposition (factoring) model is described in new detail.
Abstract: Pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNNs are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in detail.
555 citations
"Fusion of MRI and CT brain images u..." refers methods in this paper
...The method uses two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges [7]....
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...Histogram equalization is a technique that allows us to improve the contrast of images with such narrow histograms and it has been found to be a powerful technique in image enhancement [7]....
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01 Jan 2005
TL;DR: A novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise and the experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphologicalEdge detection algorithms.
Abstract: Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms
212 citations
"Fusion of MRI and CT brain images u..." refers methods in this paper
...Canny edge detection is used to perform thresh specifies sensitivity thresholds for the canny method [5]....
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...Conventionally, edge is detected according to algorithms like sobel algorithm, prewitt algorithm, canny algorithm, roberts algorithm and laplacian of gaussian algorithm [5]....
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TL;DR: Recent advances in clinical multimodality imaging, the role of correlative fusion imaging in a clinical setting, and future opportunities and challenges facing the adoption of multi-modality imaging are discussed.
58 citations
TL;DR: This work describes a hardware implementation of the contrast-limited adaptive histogram equalization algorithm (CLAHE), which is designed for the processing of image sequences from high-dynamic-range infrared cameras.
Abstract: This work describes a hardware implementation of the contrast-limited adaptive histogram equalization algorithm (CLAHE). The intended application is the processing of image sequences from high-dynamic-range infrared cameras. The variant of histogram equalization implemented is the one most commonly used today. It involves dividing the image into tiles, computing a transformation function on each of them, and interpolating between them. The contrast-limiting is modified to facilitate the hardware implementation, and it is shown that the error introduced by this modification is negligible. The latency of the design is minimized by performing its successive steps simultaneously on the same frame and by exploiting the vertical blank pause between frames. The resource usage of the histogram equalization module and how it depends on its parameters has been determined by synthesis. The design has been synthesized and tested on a Xilinx FPGA. The implementation supports substituting other dynamic range reduction modules for the histogram equalization component by partial dynamic reconfiguration.
18 citations
"Fusion of MRI and CT brain images u..." refers background in this paper
...e MRI and CT images and it is considered as a functional and anatomical images, For black and white images have 256 gray levels, from 0 to 255, and the vertical lines in the histogram[6] indicate, how many pixels in an image assume a particular gray level [11]....
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...Now, the histogram of these foreground and background image has to be displayed in the equalized form of images and finally, the histogram of foreground and background image after equalization has also been plotted[6]....
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TL;DR: It is proved that the TV-L1 based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discrete-valued set u(x) ∈ L1 ∪ L2.
Abstract: Image fusion is an imaging technique to visualize information from multiple imaging sources by one single image, which is widely used in remote sensing, medical imaging etc. In this work, we study two variational approaches to image fusion which are closely related to the standard TV-L2 and TV-L1 image approximation methods. We investigate their convex optimization formulations, under the perspective of primal and dual, and propose their associated new image decomposition models. In addition, we consider the TV-L1 based image fusion approach and study the specified problem of fusing two discrete-constrained images f1(x) ∈ L1 and f2(x) ∈ L2, where L1 and L2 are the sets of linearly-ordered discrete values. We prove that the TV-L1 based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discrete-valued set u(x) ∈ L1 ∪ L2. This extends the results for the global optimization of the discrete-constrained TV-L1 image approximation [7, 33] to the case of image fusion. As a big numerical advantage of the two proposed dual models, we show both of them directly lead to new fast and reliable algorithms, based on modern convex optimization techniques. Experiments of medical imaging, remote sensing and multi-focusing visibly show the qualitive differences between the two studied variational models of image fusion. We also apply the new variational approaches to fusing 3D medical images.
13 citations