Noor Elaiza Abdul Khalid

Seed-based region growing study for brain abnormalities segmentation

This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used controlled experimental data as our testing data. The data is designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation was done with twenty data of each category. The knowledge of the size of the abnormalities by the number of pixels were then used as the ground truth to compare with the SBRG segmentation results. The proposed SBRG technique was found to produce potential solutions to the current difficulties in detecting abnormalities in the human brain tissue area.

Empirical study of brain segmentation using particle swarm optimization

This study uses an empirical study of the efficiency of Particle swarm optimization (PSO) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this study, we used controlled experimental data as our testing data. The data is designed which that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation is done with twenty data of each category. The knowledge of the size of the abnormalities by number of pixels are then used as the ground truth to compare with the PSO segmentation results. The proposed PSO technique is found to produce potential solutions to the current difficulties in detecting abnormalities in human brain tissue area as it produced promising segmentation outcomes for light abnormalities. Nevertheless, the PSO produced poor performance in dark abnormalities segmentation as it produces low correlation values in all conditions.

The effect of sharp contrast-limited adaptive histogram equalization (SCLAHE) on Intra-oral dental radiograph images

Radiographic interpretation is crucial for the excellence of quality patient care. Image enhancements can contribute to better image that allow the clinician to derive the optimum amount of information from the radiographic data. However, the degree of enhancement applied to the original image can produce distortions that will lead to misinterpretation. This research presents the effect of Intra-oral dental x-ray images before and after processing with sharp contrast-limited adaptive histogram equalization (SCLAHE) in order to detect the effectiveness of this option on diagnostic ability. In this study 10 intra-oral periapical dental x-ray images (4 upper jaw molar and 6 lower jaw molar) were evaluated by six dentists and were scored from 1 – 5 for the overall image quality based on their perception of the image quality and the presence of the tooth pathologies. Results show that SCLAHE have the positive effect on providing better information for dentists.

CR images of metacarpel cortical edge detection-bone profile histogram approximation method

The conventional criterion for fracture risk assessment is measured based on bone mineral density (BMD). These are measured using bone densitometry machines. Although there is a strong association between bone strength and BMD, it cannot sufficiently predict fracture risk in osteoperotic patients. In view of this a more accurate measurement of bone strength is required. Bone strength are measured by geometric measurement of the bone cortical area. Cortical thickness can be measured by finding the edges of the endosteal and the periosteal of the cortical bone. Image segmentation methods could be used to find these edges. This paper presents a method of finding these edges from the radiograph of non-dominant hand. Measurements are made from metacarpal two, three and four. The edge detection module developed is based on bone profile histogram approximation algorithm. However better cortical outline can only be obtained after preprocessing the images with smoothing filters. Evaluation is done by comparing the measurements of the inner and outer cortical diameter obtained from the processed images with manual measurements using mirocalipers.

Optic cup and disc color channel multi-thresholding segmentation

Cup to disc ratio (CDR) is very important indicators in glaucoma detection. This paper proposes a method for glaucoma detection using digital fundus images with color multi-thresholding segmentation. The objective of this paper is to segment the optic cup and optic disc using color multi-thresholding segmentation and extracted feature such as cup to disc (c/d) ratio. Three ophthalmologists analysis was used as a reference to the automated segmented fundus image for a cup-to-disc ratio (CDR) measurement to discriminate glaucomatous from normal eyes. The performance of the optic cup and optic disc using color multi-thresholding segmentation has been evaluated and compared to each other. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma.

Multimodal 3-D reconstruction of human anatomical structures using surlens visualization system

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain’s blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom’s macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens’ functionality as a 3-D Multimodal Visualization System.

Adaptive Neuro-Fuzzy Inference System for brain abnormality segmentation

This paper studies the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) for segmentation of brain abnormality in MRI images. Segmentation of MRI image is an important part of brain imaging research. In this study, 150 MRI images were used as testing data for the system. The data was created by combining the shapes and size of various abnormalities and pasting it onto normal brain image. Several types of backgrounds were tested — low, medium and high grey levels. The experimental results show good segmentation for medium and low background levels value for both light and dark abnormality levels over different backgrounds.

Locating abnormalities in brain blood vessels using parallel computing architecture

CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.

Fish Bone Impaction Using Adaptive Histogram Equalization (AHE)

This paper presents the enhancement capability of adaptive histogram equalization (AHE) on the soft tissue lateral neck radiograph for suspected fish bone ingestion. Embedded fish bone lodge in the throat is not easily visible in unprocessed plain radiograph. Serious complication may cause perforation of the lodged and inflammation that can progress to abscess. Forty X-ray images of 23 male and 17 female patients between the ages of 6 to 72 years from different ethnic groups were collected from Hospital Sungai Buloh and Pusat Perubatan Hospital Universiti Kebangsaan Malaysia (PPUKM). Due to the high resolution, the images were crop before being processed using adaptive histogram equalization. The quality of the image was assessed and evaluated during pre and post processing by the radiologists. The result shows AHE as a promising contrast enhancement for detection of fish bone in soft tissue at the lateral neck radiographs.

Evaluation of Sobel, Canny, Shen & Castan using sample line histogram method

This paper introduces a sample line histogram method (SLHM) in evaluating the accuracy in detecting bone edges. CR images of bone joints namely the knee and the elbow joints are used in this experiment. The purpose of the project is to identify the suitable method for bone cortical edge detection. The performance of three edge detection method: Sobel, Canny and Shen & Castan are compared. The resulting images are evaluated by visual inspection and SLHM.