Hamdan Amin

Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach

This paper presents an efficient algorithm for segmenting different types of pulmonary nodules including high and low contrast nodules, nodules with vasculature attachment, and nodules in the close vicinity of the lung wall or diaphragm. The algorithm performs an adaptive sphericity oriented contrast region growing on the fuzzy connectivity map of the object of interest. This region growing is operated within a volumetric mask which is created by first applying a local adaptive segmentation algorithm that identifies foreground and background regions within a certain window size. The foreground objects are then filled to remove any holes, and a spatial connectivity map is generated to create a 3-D mask. The mask is then enlarged to contain the background while excluding unwanted foreground regions. Apart from generating a confined search volume, the mask is also used to estimate the parameters for the subsequent region growing, as well as for repositioning the seed point in order to ensure reproducibility. The method was run on 815 pulmonary nodules. By using randomly placed seed points, the approach was shown to be fully reproducible. As for acceptability, the segmentation results were visually inspected by a qualified radiologist to search for any gross misssegmentation. 84% of the first results of the segmentation were accepted by the radiologist while for the remaining 16% nodules, alternative segmentation solutions that were provided by the method were selected.

Automated detection of lung nodules in CT images using shape-based genetic algorithm.

A shape-based genetic algorithm template-matching (GATM) method is proposed for the detection of nodules with spherical elements. A spherical-oriented convolution-based filtering scheme is used as a pre-processing step for enhancement. To define the fitness function for GATM, a 3D geometric shape feature is calculated at each voxel and then combined into a global nodule intensity distribution. Lung nodule phantom images are used as reference images for template matching. The proposed method has been validated on a clinical dataset of 70 thoracic CT scans (involving 16,800 CT slices) that contains 178 nodules as a gold standard. A total of 160 nodules were correctly detected by the proposed method and resulted in a detection rate of about 90%, with the number of false positives at approximately 14.6/scan (0.06/slice). The high-detection performance of the method suggested promising potential for clinical applications.

Volumetric Quantification of Atherosclerotic Plaque in CT Considering Partial Volume Effect

Coronary artery calcification (CAC) is quantified based on a computed tomography (CT) scan image. A calcified region is identified. Modified expectation maximization (MEM) of a statistical model for the calcified and background material is used to estimate the partial calcium content of the voxels. The algorithm limits the region over which MEM is performed. By using MEM, the statistical properties of the model are iteratively updated based on the calculated resultant calcium distribution from the previous iteration. The estimated statistical properties are used to generate a map of the partial calcium content in the calcified region. The volume of calcium in the calcified region is determined based on the map. The experimental results on a cardiac phantom, scanned 90 times using 15 different protocols, demonstrate that the proposed method is less sensitive to partial volume effect and noise, with average error of 9.5% (standard deviation (SD) of 5-7 mm3) compared with 67% (SD of 3-20 mm3) for conventional techniques. The high reproducibility of the proposed method for 35 patients, scanned twice using the same protocol at a minimum interval of 10 min, shows that the method provides 2-3 times lower interscan variation than conventional techniques

Compression of Digital Medical Images Based on Multiple Regions of Interest

Advances in digital medical imaging technologies, particularly magnetic resonance imaging and multi-detector CT (Computed Tomography), have resulted in substantial increase in the size of datasets, as a result of improvement in spatial and temporal resolution. In order to reduce the storage cost, diagnostic analysis cost and transmission time without significant reduction of the image quality, a state of the art image compression technique is required. We implemented a context-based and regions of interest (ROI) based approach to compress medical images in particular vascular images, where a high spatial resolution and contrast sensitivity is required in areas such as stenosis. The vascular image is divided into: the primary region of interest (PROI), the secondary region of interests (SROI) and the background. The PROI can be a stenosis of vessel and it is identified manually by the radiologist. The SROI is divided into other parts or regions among which the most important level is represented by vessels. The other levels are the other tissues or part of the body and the last level is the background region. The SROI is detected automatically by an in house 3D region growing algorithm. The PROI is considered as a seed for region growing. The proposed lossy-to-lossless region-based compression method is compressed these multiple ROIs at various degrees of interest and at higher precision (up to lossless) than other areas such as background. To demonstrate the result of this algorithm, this method is applied on peripheral arteries images (up to 2000 images) and the result have been compared with standard Jpeg2000 on 10 datasets. The size of compressed images can be reduced up to 67 percent

Computer Aided Detection and Measurement of Abdominal Aortic Aneurysm Using Computed Tomography Digital Images

Computer-aided detection (CAD) systems, which automatically detect and indicate location of potential abnormalities in scan digital images, have the capacity to increase the accuracy of the radiologists’ interpretations and finding. This paper presents an efficient new CAD .for automatic and accurate detection and quantification of Abdominal Aortic Aneurysm (AAA). The system first detects and extracts the lumen and then identifies the location of the abdominal aortic from the total lumen. The extracted abdominal aortic lumen is then used as an initial surface to segment the abdominal aorta which might contain aneurysm. The geometrical and morphological features of both lumen and aorta are examined for the presence of aneurysm based on predefined criteria set by incorporating prior understanding of the normal expected variation of aorta. The experimental result of the proposed system on 60 CTA datasets indicated a 98% success in detection (CAD) and a 95% in segmentation results (CAM).

Automatic identification of colonic polyp in high-resolution CT images

Automatic polyp detection is a challenging task as polyps come in different sizes and shape. The detection generally consists of colon segmentation, identification of suspected polyps and classification. Classification involves discriminating polyps from among many suspected regions based on a number of features extracted from the detected regions. This paper presents the work on the first two stages of the detection. For the colon segmentation, the fuzzy connectivity region growing technique is used while for the identification of suspected polyps concave region searching is applied. A rule-based filtering based on 3D volumetric features is used to reduce a large number of non-polyp structures (false positives). The method is fast, robust and validated with a number of high-resolution colon datasets.

Automatic polyp detection of colon using high resolution CT scans

Automatic detection of polyps can be a valuable tool for diagnoses of early colorectal cancer as early detection and hence removal of polyps can save life. Polyp detection is a challenging task as polyps come in different sizes and shapes. The detection generally consists of three stages: 1) colon segmentation, 2) identification of suspected polyps and 3) polyp classification. The latter involves classifying polyps from among many suspected regions. This paper concentrates on the first two stages of the detection. For the colon segmentation, the fuzzy connectivity region growing technique is used while for the identification of suspected polyps concave region searching is applied. The method is fast, robust and validated with a number of high-resolution colon datasets.

An adaptive 3D region growing algorithm to automatically segment and identify thoracic aorta and its centerline using Computed Tomography Angiography Scans

Thoracic Aortic Aneurysm (TAA) is a localized swelling of the thoracic aorta. The progressive growth of an aneurysm may eventually cause a rupture if not diagnosed or treated. This necessitates the need for an accurate measurement which in turn calls for the accurate segmentation of the aneurysm regions. Computer Aided Detection (CAD) is a tool to automatically detect and segment the TAA in the Computer tomography angiography (CTA) images. The fundamental major step of developing such a system is to develop a robust method for the detection of main vessel and measuring its diameters. In this paper we propose a novel adaptive method to simultaneously segment the thoracic aorta and to indentify its center line. For this purpose, an adaptive parametric 3D region growing is proposed in which its seed will be automatically selected through the detection of the celiac artery and the parameters of the method will be re-estimated while the region is growing thorough the aorta. At each phase of region growing the initial center line of aorta will also be identified and modified through the process. Thus the proposed method simultaneously detect aorta and identify its centerline. The method has been applied on CT images from 20 patients with good agreement with the visual assessment by two radiologists.