Sobri Muda

Automated segmentation of brain lesion based on diffusion-weighted MRI using a split and merge approach

This paper presents a segmentation of brain lesion from diffusion-weighted magnetic resonance images (DW-MRI or DWI) using a split and merge approach. The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, the split and merge algorithm is designed to segment the lesion. Histogram thresholding technique is used at each split level to detect pixels with either hyperintense or hypointense. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. The analysis shows that mean and number of lesion pixels are the best homogeneity criteria. Hyperintense and hypointense lesions are segmented automatically by merging the regions that are homogenous according to the criteria.

Brain lesion segmentation of Diffusion-weighted MRI using gray level co-occurrence matrix

This paper presents an automated segmentation of brain lesion from Diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on region and boundary information in gray level co-occurrence matrix (GLCM). The lesions are hyperintense lesion from tumour, acute infarction, haemorrhage and abscess, and hypointense lesion from chronic infarction and haemorrhage. Pre-processing is applied to the DWI for intensity normalization, background removal and intensity enhancement. Then, GLCM is computed to segment the lesions. Different peaks from the GLCM cross-section indicate the present of normal brain region, cerebral spinal fluid (CSF), hyperintense or hypointense lesions. Minimum and maximum threshold values are computed from the GLCM cross-section. Region and boundary information from the GLCM are introduced as the statistical features for segmentation of hyperintense and hypointense lesions. The proposed method provides very good segmentation results even in a small brain lesion.

Segmentation of brain lesions in diffusion-weighted MRI using thresholding technique

This paper presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on thresholding technique. The lesions are solid tumour, acute infarction, haemorrhage, and abscess. Preprocessing is applied to the DWI for normalization, background removal and enhancement. Two different techniques which are Gamma-law transformation and contrast stretching are applied for the enhancement. For the image segmentation process, the DWI is divided by 8×8 regions. Then image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. By using Gamma-law transformation, 0.48 is found as the optimal thresholding value whereas 0.28 for the contrast stretching. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), and false negative rate (FNR). Thresholding with gamma-law transformation algorithm provides better segmentation results with AO, FPR, FNR (0.68, 0.14, 0.18) compared to contrast stretching (0.62, 0.15, 0.23).

Image-guided chemoport insertion by interventional radiologists: A single-center experience on periprocedural complications

Purpose: To report our early experience in image-guided chemoport insertions by interventional radiologists. Materials and Methods: This was a cross-sectional study conducted in a tertiary center with 161 chemoport insertions done from June 2008 to June 2010. The chemoports were inserted either at the angiography suite or at the mobile operation theater unit. Ninety percent of the chemoports had right internal jugular vein (IJV) as the entry site. Other entry sites included the left IJV, subclavian veins and the inferior vena cava. Immediate and early complications were recorded. All insertions were performed under image guidance with the aid of ultrasound and fluoroscopy. Results: The technical success rate was 99.4%. In terms of immediate complications, there were only two cases of arterial puncture that resolved with local compression. No pneumothorax or air embolism was documented. Twenty-six early complications were recorded. The most common early complication was catheter blockage (12/161; 7.4%), followed by catheter-related infection (9/161; 5.6%). Other complications were catheter malposition, venous thrombosis and catheter dislodgement or leak. A total of 11 (6.8%) chemoports had to be removed within 30 days; most of them were due to infections that failed to respond to systemic antibiotic therapy. In terms of place of procedure, there were no significant differences in complication rates between the angiography suite and the mobile operation theater unit. Conclusion: Image-guided chemoport insertion by interventional radiologist gives low periprocedural complication rates. Using right IJV as the entry site, the image guidance gives good success rate with least complication.

Cerebral vessel enhancement using bilateral and Hessian-based filter

Vessel enhancement in magnetic resonance angiography (MRA) is an important preprocessing step for stroke surgical planning and further processing. Bilateral filter has been widely used to reduce noise due to its ability for smoothing an image and preserve the edges. It suffers a drawback of over smoothing that leads to discontinuity of blood vessels when applied to MRA image. Hessian-based filter is known to have the ability of enhancing the vessels and preserving the geometrical structure. This paper presents the vessel enhancement technique which combines bilateral and Hessian-based filters to exploit the advantages of them. The bilateral filtered images show that weighted bilateral filter can reduce more noise when comparing the peak signal-to-noise ratio (PSNR) value. Then, Hessian-based filter is performed on several types of preprocessed images to compare the performance of this technique on different types of images. Our method shows a promising result in suppressing the noise that is enhanced by Hessian-based filter.

Brain Lesion Segmentation of Diffusion-Weighted MRI Using Thresholding Technique

This paper presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on thresholding technique. The lesions are solid tumor, acute infarction, haemorrhage, and abscess. Pre-processing is applied to the DWI for normalization, background removal and enhancement. Two different techniques which are Gamma-law transformation and contrast stretching are applied for the enhancement. For the image segmentation process, the DWI is divided by 8 x 8 regions. Then image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. By using Gamma-law transformation, 0.48 is found as the optimal thresholding value whereas 0.28 for the contrast stretching. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), and false negative rate (FNR). Thresholding with gamma-law transformation algorithm provides better segmentation results compared to contrast stretching technique. The proposed technique provides good brain lesion segmentation results even though the simplest segmentation technique is used.