Mass Segmentation Techniques For Lung Cancer CT Images

Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method

Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms

Segmentation of 3D medical images based on region growing method

Táto bakalárska práca sa zaoberá segmentáciou medicínskych objemových dát pomocou metódy narastania oblastí. Cieľom je popísať hlavné metódy 3D segmentácie obrazových dát a zamerať sa najmä na metódu narastania oblastí. Vstupnými dátami sú snímky rezov mozgu z magnetickej rezonancie, ktoré je možné pomocou navrhnutého prehliadača zobrazovať v troch základných rovinách. Prehliadač je realizovaný v programovom prostredí Matlab. Segmentácia obrazových dát je realizovaná metódou semienkového narastania oblastí.This bachalor thesis deals with a region growing approach for segmentation of volumetric medical images. The aim is to present basic methods of segmentation of image data and to focus in particular on the approach of region growing. The input data are brain slices of magnetic resonance imaging which can be visualized using the browser into the three basic planes. The viewer is implemented in MATLAB programming environment. Image segmentation is realized by seeded region growing.

Detection and description of pulmonary nodules through 2D and 3D clustering

Precise 3D automated detection, description and classification of pulmonary nodules offer the potential for early diagnosis of cancer and greater efficiency in the reading of computerised tomography (CT) images. CT scan centres are currently experiencing high loads and experts shortage, especially in developing countries such as Iraq where the results of the current research will be used. This motivates the researchers to address these problems and challenges by developing automated processes for the early detection and efficient description of cancer cases. This research attempts to reduce workloads, enhance the patient throughput and improve the diagnosis performance. To achieve this goal, the study selects techniques for segmentation, classification, detection and implements the best candidates alongside a novel automated approach. Techniques for each stage in the process are quantitatively evaluated to select the best performance against standard data for lung cancer. In addition, the ideal approach is identified by comparing them against other works in detecting and describing pulmonary nodules. This work detects and describes the nodules and their characteristics in several stages: automated lung segmentation from the background, automated 2D and 3D clustering of vessels and nodules, applying shape and textures features, classification and automatic measurement of nodule characteristics. This work is tested on standard CT lung image data and shows promising results, matching or close to experts’ diagnosis in the nodules number and their features (size/volume, location) and in terms the accuracy and automation. It also achieved a classification accuracy of 98% and efficient results in measuring the nodules’ volume automatically

Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou