Improvement of Single Seeded Region Growing Algorithm on Image Segmentation

To form a hybrid approach for image segmentation, several researches have been done to combine some techniques for better improvements. This article is concerned with image segmentation using combined methods. To separate foreground from background in image the pixel intensities have been considered. For image segmentation region growing with seed pixel is one of the most important segmentation methods. In single seeded region growing, it is very difficult to find out the proper position of the pixel during the selection. By considering the limitation of single seeded region growing an improved algorithm for region growing has proposed. The position of the seed pixel can be chosen before growing the region for segmentation using the proposed technique. Then combine this method with existing single seeded region growing algorithm. After the comparison using segmentation evaluation parameters it can be seen that, this combined method works better than others existing methods

Visualization and image based characterization of hydrodynamic cavity bubbles for kidney stone treatment

Accurate detection, tracking and classification of micro structures through high speed imaging are very important in many biomedical applications. In particular, visualization and characterization of hydrodynamic cavity bubbles in breaking kidney stones have become a real challenge for researchers. Various micro imaging techniques have been used to monitor either an entire bubble cloud or individual bubbles within the cloud. The main target of this thesis is to perform an image based characterization of hydrodynamic cavity bubbles for kidney stone treatment by designing and constructing a new imaging setup and implementing several image processing and computer vision algorithms for detecting, tracking and classifying cavity bubbles. A high speed CMOS camera with a long distance microscope illuminated by 2 pulsed 198 high performance LED arrays is designed. This system and a μ-PIV setup are used for capturing images of high speed bubbles. Several image processing algorithms including median and morphological filters, segmentation, edge detection and contour extraction algorithms are extensively used for the detection of the bubbles. Furthermore, incremental selftuning particle filtering (ISPF) method is utilized to track the motion of the high speed cavity bubbles. These bubbles are also classified by their geometric features such as size, shape and orientation. An extensive visualisation work is conducted on the new setup and cavity bubbles are successfully detected, tracked and classified from the microscopic images. Despite very low exposure times and high speed motion of the bubbles, developed system and methods work in a very robust manner. All the algorithms are implemented in Microsoft Visual C++ using OpenCV 2.4.2 library

Slantlet transform-based segmentation and α -shape theory-based 3D visualization and volume calculation methods for MRI brain tumour

Magnetic Resonance Imaging (MRI) being the foremost significant component of medical diagnosis which requires careful, efficient, precise and reliable image analyses for brain tumour detection, segmentation, visualisation and volume calculation. The inherently varying nature of tumour shapes, locations and image intensities make brain tumour detection greatly intricate. Certainly, having a perfect result of brain tumour detection and segmentation is advantageous. Despite several available methods, tumour detection and segmentation are far from being resolved. Meanwhile, the progress of 3D visualisation and volume calculation of brain tumour is very limited due to absence of ground truth. Thus, this study proposes four new methods, namely abnormal MRI slice detection, brain tumour segmentation based on Slantlet Transform (SLT), 3D visualization and volume calculation of brain tumour based on Alpha (α) shape theory. In addition, two new datasets along with ground truth are created to validate the shape and volume of the brain tumour. The methodology involves three main phases. In the first phase, it begins with the cerebral tissue extraction, followed by abnormal block detection and its fine-tuning mechanism, and ends with abnormal slice detection based on the detected abnormal blocks. The second phase involves brain tumour segmentation that covers three processes. The abnormal slice is first decomposed using the SLT, then its significant coefficients are selected using Donoho universal threshold. The resultant image is composed using inverse SLT to obtain the tumour region. Finally, in the third phase, four original ideas are proposed to visualise and calculate the volume of the tumour. The first idea involves the determination of an optimal α value using a new formula. The second idea is to merge all tumour points for all abnormal slices using the α value to form a set of tetrahedrons. The third idea is to select the most relevant tetrahedrons using the α value as the threshold. The fourth idea is to calculate the volume of the tumour based on the selected tetrahedrons. In order to evaluate the performance of the proposed methods, a series of experiments are conducted using three standard datasets which comprise of 4567 MRI slices of 35 patients. The methods are evaluated using standard practices and benchmarked against the best and up-to-date techniques. Based on the experiments, the proposed methods have produced very encouraging results with an accuracy rate of 96% for the abnormality slice detection along with sensitivity and specificity of 99% for brain tumour segmentation. A perfect result for the 3D visualisation and volume calculation of brain tumour is also attained. The admirable features of the results suggest that the proposed methods may constitute a basis for reliable MRI brain tumour diagnosis and treatments

Natural interactions: an application for gestural hands recognition

Dissertação de Mestrado em Desenvolvimento de Software e Sistemas Interativos apresentada à Escola Superior de Tecnologia do Instituto Politécnico de Castelo Branco.Este trabalho apresenta um sistema para o desenvolvimento de novas interfaces homem-máquina com foco no reconhecimento de gestos estáticos de mãos humanas. A proposta é auxiliar o acesso a certos objetos para o ocupante de uma cadeira de rodas inteligente, a fim de facilitar a sua vida diária. A metodologia proposta baseia-se no uso de processos computacionais simples e de hardware de baixo custo. O seu desenvolvimento envolve uma abordagem completa aos problemas de visão computacional, com base nas etapas da captura de imagem de vídeo, segmentação de imagens, extração de características, reconhecimento e classificação de padrões. A importância deste trabalho relaciona-se com a necessidade de construir novos modelos de interação que permitam, de uma forma natural e intuitiva, a simplificação da vida quotidiana de uma pessoa com dificuldades motoras.Abstract: This thesis presents a system for the development of new human-machine interfaces focused on static gestures recognition of human hands. The proposal is to give access to certain objects to the occupant of an intelligent wheelchair in order to facilitate their daily life. The proposed methodology relies on the use of simple computational processes and low-cost hardware. Its development involves a comprehensive approach to the problems of computer vision, based on the steps of the video image capture, image segmentation, feature extraction, pattern recognition and classification. The importance of this work relates to the need to build new models of interaction that allow, in a natural and intuitive way, to simplify the daily life of a disable person

Segmentace a odhad hustoty ve stehennim kloubu

There are two basic ways of surgical treatment of femoral neck fracture - total endoprosthesis or a special metal screw in the bone. In case of a dense enough bone the screws are preferred. But for a too parse bone, the screw may cut through the femoral head, which results in further surgical operations. We present a novel method for femoral head density measurement, which serves as another hint for the doctor's decision, whether to apply a screw or total endoprosthesis. Our approach is based on semi-automatical femoral head segmentation from CT dataset based on finding optimal path through polar coordinates on axial slices. The cost function is based on a combination of corticallis properties, mostly the directional behavior of 3D gradients and their size in 2D slices, where they form typical "channels". The final volume is computed using filling and morphological algorithms and its properties are further measured. The final implementation was experimentally validated on RTG clinic of Bulovka hospital and allows radiologists to intuitively and accurately estimate the femoral head density in approximately 1 to 3 minutes.Existují dvě hlavní metody pro léčbu zlomenin krčku stehenní kosti - totální endoprotéza a spojení speciálním kovovým šroubem. Při dostatečně kvalitní kosti je preferován šroub. Ovšem pokud je příliš řídká, šroub se může proříznout kloubní hlavicí, což vede k dalším operacím. Tato práce se zabývá novou metodou pro měření hustoty kostní trámčiny kloubní hlavice, které slouží lékaří jako další vodítko pro rozhodnutí, zda aplikovat šroub nebo totální endoprotézu. Náš přístup je založen na poloautomatické segmentaci kloubní hlavice z CT dat, založené na hledání optimální cesty v polárních souřadnicích na axiálních řezech, kde základ cenové funkce tvoří kombinace vlastností okostice, zejména směrový charakter 3D gradientů a velikost gradientů ve 2D řezech, kde tvoří typické "kanálky" . Pomocí vyplňování a morfologických operací je pak vytvořen objem, jehož vlastnosti jsou dále měřeny. Výsledná implementace byla experimentálně ověřena na RTG klinice FN na Bulovce a umožňuje RTG specialistovi intuitivně, pomocí graffického rozhraní vytvořit přesný odhad hustoty kloubní hlavice v rozpětí 1 až 3 minut.Department of Software and Computer Science EducationKatedra softwaru a výuky informatikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic