Fusion of MRI and CT images using guided image filter and image statistics

In medical imaging using different modalities such as MRI and CT, complementary information of a targeted organ will be captured. All the necessary information from these two modalities has to be integrated into a single image for better diagnosis and treatment of a patient. Image fusion is a process of combining useful or complementary information from multiple images into a single image. In this article, we present a new weighted average fusion algorithm to fuse MRI and CT images of a brain based on guided image filter and the image statistics. The proposed algorithm is as follows: detail layers are extracted from each source image by using guided image filter. Weights corresponding to each source image are calculated from the detail layers with help of image statistics. Then a weighted average fusion strategy is implemented to integrate source image information into a single image. Fusion performance is assessed both qualitatively and quantitatively. Proposed method is compared with the traditional and recent image fusion methods. Results showed that our algorithm yields superior performance.     

Mapping stereo and image matching algorithms onto parallel architectures

In this paper we present parallel implementations of two vision tasks; stereo matching and image matching. Linear features are used as matching primitives. These implementations are performed on a fixed size mesh array and achieve processor-time optimal performance. For stereo matching, we proposeO(Nn ³/P ²) time algorithm on aP ×P processor mesh array, whereN is the number of line segments in one image,n is the number of line segments in a window determined by the object size, andP ⩽n. The sequential algorithm takesO(Nn ³) time. For image matching, a partitioned parallel implementation is developed.O[((nm/P ²) +P)nm] time performance is achieved on aP ×P processor mesh array, whereP ² ⩽nm. This leads to a processor-time optimal solution forP ⩽ (nm)^1/3. This research was supported in part bynsf under grantiri-9145810 and in part bydarpa andafosr contracts F-49260-89-C-0126 and F-49620-90-C-0078.     

A new approach to medical image stitching using minimum average correlation energy filter and peak to side‐lobe ratio

Standard X‐ray images using conventional screen‐film technique have a limited field of view and failed to visualize the entire long bone on a single image. To produce images with whole body parts, digitized images from the films that contain portions of the body parts are assembled using image stitching. This article presents a new medical image stitching method that uses minimum average correlation energy filters to identify and merge pairs of X‐ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases that contain a total of 40 pairs of overlapping and nonoverlapping images. Then the experimental results are compared to those of the normalized cross correlation (NCC) method. It is found that the proposed method outperforms the NCC method in identifying both the overlapping and nonoverlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about five times shorter than that of the NCC method. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 166–171, 2012     

Depth recovery and refinement from a single image using defocus cues

We present a technique to recover and refine the depth map from a single image captured by a conventional camera in this paper. Our method builds on the universal imaging principle: only scene at the focus distance will converge to a single sharp point on imaging sensor but other scene will yield different blur effects varying with its distance from the camera lens. We first estimate depth values at edge locations via spectrum contrast and then recover the full depth map using a depth matting optimization method. Due to the fact that some blur textures such as soft shadows or blur patterns will produce ambiguity results during the procedure of depth estimation, we use a total variation-based image smoothing method to smooth the original image, a smoothed image with detailed texture being suppressed can be generated. Taking this smoothed image as reference image, a guided filter is used to refine the final depth map.     

Guided image filtering using data mining and decomposition

Owing to local linear model, guided image filter may contain from ringing artefacts. A modified guided image filter based on data mining techniques and decomposition is proposed in this paper. Image is split into patches, passing through the guided filter, clustered into number of clusters and transformed into compact domain using principal component analysis. Edges are preserved and halo artefacts are removed using detail enhancement based on weighted least square. Simulation results demonstrate the strength of the proposed technique as compared with other techniques especially around the edges where noise and halo artefacts may be present in the image.     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques

Tissues in brain are the most complicated parts of our body, a clear examination and study are therefore required by a radiologist to identify the pathologies. Normal magnetic resonance (MR) scanner is capable of producing brain images with bounded tissues, where unique and segregated views of the tissues are required. A distinguished view upon the images is manually impossible and can be subjected to operator errors. With the assistance of a soft computing technique, an automated unique segmentation upon the brain tissues along with the identification of the tumor region can be effectively done. These functionalities assist the radiologist extensively. Several soft computing techniques have been proposed and one such technique which is being proposed is PSO‐based FCM algorithm. The results of the proposed algorithm is compared with fuzzy C‐means (FCM) and particle swarm optimization (PSO) algorithms using comparison factors such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy (energy function), Jaccard (Tanimoto Coefficient) index, dice overlap index and memory requirement for processing the algorithm. The efficiency of the PSO‐FCM algorithm is verified using the comparison factors.     

Detection of brain tumour from MRI image using modified region growing and neural network

Abstract

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. It consists of four steps which includes: (i) pre-processing; (2) modified region growing by the inclusion of an additional orientation constraint in addition to the normal intensity constrain; (3) feature extraction of the region; and (4) final classification using the neural network. The performance of the proposed technique is systematically evaluated using the magnetic resonance imaging (MRI) brain images received from the public sources. For validating the effectiveness of the modified region growing, we have considered the quantity rate parameter. For the evaluation of the proposed technique of tumour detection, we make use of sensitivity, specificity and accuracy values which we compute from finding out false positive, false negative, true positive and true negative. Comparative analyses were made of the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. From the results obtained, we could see that the proposed technique achieved the accuracy of 80% for the testing dataset, which clearly demonstrated the effectiveness of the modified region growing when compared to the normal technique.