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The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron...
CT image based lung nodule detection is the most widely used and accepted method for detecting lung cancer. Most CT image based methods are based on supervised/unsupervised learning, which has a high number of false positives and needs a large amount pre-segmented training samples. This problem can be solved, if a set of optimally small number of training samples can be created, where each sample...
This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The...
For detecting pleural effusion and pneumothorax, which affect the pleural membranes of the lungs, Computer Aided Diagnosis system (CAD) is proposed. The chest CT slices are initially preprocessed to remove the Gaussian noise by using a sigma filter segmentation technique used here extracts the lungs and the regions affected by pleural effusion using conventional thresholding techniques like Otsu's...
Among five main type of cancer lung cancer is one of causing health hazards in both men and women all over the world. Advanced techniques of Computed Tomography and medical images play an important role in clinically detection of lung cancer tumors in all TNM stages. Efficient Computer Aided Detection (CADe) systems help the radiologist in early detection and diagnosis of lung cancer. The objective...
Automatic pulmonary nodule detection in Computer Tomographic (CT) images is a challenge task for the Computer Aided Diagnosis (CAD) systems. This paper proposes a novel nodule enhancement filter (Homocentric Squares Filter) for automatic nodule detection based on CT characteristic and shape feature. First, the bright regions in the image are enhanced by calculating the CT value variation between an...
Lung cancer is the most deadliest disease all over the world. It is caused by uncontrolled growth of abnormal cells which leads to formation of lumps called nodules in the lung. Now days, the image processing techniques are extensively used in numerous medical areas to increase the survival rate. The paper includes comparison between three segmentation techniques namely iterative thresholding, Region...
We developed an automated lung segmentation method, which uses deformable model with sparse shape composition prior for patients with compromised lung volumes with severe pathologies in CT. Fifteen thoracic computed tomography scans for patients with lung tumors were collected and reference lung ROIs in each scan was manually segmented to assess the performance of the method. First, sparse shape composition...
In this study we analyze lung shape change between the upright and supine postures and the effect of this shape change on the deformation of lung tissue under gravity. We use supine computed tomography images along with upright tomosynthesis images obtained on the same day to show that there is significant diaphragmatic movement between postures. Using a continuum model of lung tissue deformation...
In this paper, a novel fully automatic method of extraction of lung parenchyma is presented. Combining the iterative gray-level thresholds selection and the pulmonary regions extraction with error detection in 2D image, seed points and threshold are fast determined. In consequence, the pulmonary airspace is detected with 3D region growing method. Two steps airways segmentation with additional shape...
Lymph nodes play an important role in clinical practice but detection is challenging due to low contrast surrounding structures and variable size and shape. In this paper, we propose a fully automatic method for mediastinal lymph node detection and station mapping on thoracic CT scans. First, lungs are automatically segmented to locate the mediastinum region. Shape features by Hessian analysis, local...
A novel method for the automatic segmentation of the lung in X-ray computed tomography (CT) images is presented. In this paper, a maximum a posteriori (MAP) estimation framework, combining neighbor prior information and image gray level information, is used to extract the boundary of lung. The relationship of the left lung and the right lung is represented as a joint density function. We use the principal...
This paper describes a novel variational approach for segmentation of small-size lung nodules which may be detected in low dose CT (LDCT) scans. These nodules do not possess distinct shape or appearance characteristics; hence, their segmentation is enormously difficult, especially at small size (≤ 1cm). Variational methods hold promise in these scenarios despite the difficulties in estimation of the...
A new graph-based approach for simultaneous segmentation of lungs in 4D CT scans is presented. The approach is based on a “just enough” user interaction principle and consists of two stages. First, a fully automated graph-based segmentation algorithm is applied. Second, the user inspects the result and can correct local segmentation errors with all interactions performed within the graph-based computer-aided...
An automatic segmentation method for extraction of human rib structures from chest CT volume data is presented. Segmentation is initiated from the middle coronal slice to attain complete and isolated 12 pairs of ribs with a recursive tracking on coronal slices spreading from the middle coronal slice. At each coronal slice, the lung contours are extracted, candidate rib regions are derived from thresholding,...
Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using automatic seed point placement levels sets...
We present a simple and elegant method to incorporate user input in a template-based segmentation method for diseased organs. The user provides a partial segmentation of the organ of interest, which is used to guide the template towards its target. The user also highlights some elements of the background that should be excluded from the final segmentation. We derive by likelihood maximization a registration...
Philips has introduced the world's first whole body sequential PET/MR system. We present the current status of MR-based attenuation correction (MRAC) technique. MRAC consists of MR image acquisition, segmentation, truncation compensation (TC), μ-value assignment, as well as correction for patient table and RF coils. These components have been described last year; this paper focuses on updates of the...
Lung cancer is one of the most frequently occurring cancer and has a very low five-year survival rate. Computer-aided diagnosis (CAD) helps reducing the burden of radiologists and improving the accuracy of abnormality detection during CT image interpretations. Owing to rapid development of the scanner technology, the volume of medical imaging data is becoming huger and huger. Automated segmentations...
A method to register the expiration and inspiration breath-hold HRCT lung image volumes was presented. We considered that the deformation between the expiration and inspiration of lung can be decomposed into a global affine transformation and a local deformation. Before registration, we segmented the lung parenchyma from thoracic HRCT slices. Then, we used a prior anatomy knowledge based method to...
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