Detection of Spectral Signatures in Multispectral MR Images for Classification

[[abstract]]This paper presents a new spectral signature detection approach to magnetic resonance (MR) image classification. It is called constrained energy minimization (CEM) method, which is derived fromthe minimum variance distortionless response in passive sensor array processing. It considers a bank of spectral channels as an array of sensors where each spectral channel represents a sensor and object spectral signature in multispectral MR images are viewed as signals impinging upon the array. The strength of the CEM lies on its ability in detection of spectral signatures of interest without knowing image background. The detected spectral signatures are then used for classification. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. Unlike most spatial-based classification techniques, the proposed CEM takes advantage of spectral characteristics to achieve object detection and classification. A series of experiments is conducted and compared with the commonly used-means method for performance evaluation. The results showthat the CEM method is a promising and effective spectral technique for MR image classification

Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems

A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST and K-means

3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods

Image-quality figure evaluator based on contrast-detail phantom in radiography

Background: In radiology, it is significantly important to produce adequate diagnostic information while minimally affecting the patient with the lowest amount of dose. A contrast-detail phantom is generally used to study the quality of image and the amount of radiation dose for digital X-ray imaging systems. To evaluate the quality of a phantom image, radiologists are traditionally required to manually indicate the location of the holes in each square in the phantom image. Then, the image quality figure (IQF) of the image can be evaluated. However, evaluation by the human eye is subjective as well as time-consuming, and it differs from person to person. Methods: In this paper, an image processing-based IQF evaluator is proposed to automatically measure the quality of a phantom image. Nine phantom images, each consisting of 2382 2212 pixels, were used as test images and were provided by Taichung Hospital, Department of Health, Executive Yuan, Taiwan, Republic of China. The IP-IQF evaluator separates the phantom image into squares and then stretches the contrast of each square to the range 0–255. After that, it splits each square into 3 3 equal-sized regions, and recognizes the pattern of the square based on the features computed by mean-difference gradient operation and run length enhancer. Furthermore, a genetic algorithm- based parameter values-detecting algorithm is presented to compute the optimal values of the parameters used in the IP-IQF evaluator. Results: The experimental results demonstrate that CoCIQ and the IP-IQF evaluator can efficiently measure the IQF of a phantom image. The IP-IQF evaluator is more effective than a radiologist and CoCIQ in evaluating the IQF of a phantom image. Conclusions: The proposed IQF evaluator is more sensitive than not only the observation of radiologists but also the computer program CoCIQ. Moreover, a genetic algorithm is provided to compute the most suitable values of the parameters used in the IQF evaluator

Application of Artificial Immune System Approach in MRI Classification

Numerous scholars have submitted the theory and research of artificial immune systems (AISs) in recent years. Although AIS has been used in various fields, applying the AIS to medical images is very rare. The purpose of this study is using the clonal selection algorithm (CSA) of artificial immune systems for classifying the brain MRI, and displaying a single organism image which can finally offer faster organism reference information to a doctor; hence reducing the time to ascertain large number of images, so that the doctor can diagnose the nidus more efficiently and accurately. In order to verify the feasibility and efficiency of this method, we adopt statistical theory for manifold assessment and compare with the perceptron network of double layers, FCM method. The result proves that the method of this study is both feasible and useful

A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

The Opinion newspaper issue dated March 9, 1972https://digitalcommons.law.buffalo.edu/the_opinion/1046/thumbnail.jp