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Ovarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).<p>Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.<p>A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired <i>in vivo</i> were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. <p>The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.<p>The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm.

Ovarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).<p>Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.<p>A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired <i>in vivo</i> were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. <p>The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.<p>The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm.

A DSP-based system is developed in this thesis for tracking ¡§an arbitrary-shaped object¡¨. It uses CCD camera to capture images, and detects in the video sequence. When we want to track a target that we interest, we can make the target in the view of camera. If the target move, the system will lock it and extract its contour by using active contour model. After extracting contour, the system will start to track target and shows the locked image on the LCD screen. The tracking system includes three sub-systems : ¡§Moving Object Detection¡¨, ¡§Active Contour Model¡¨, and ¡§Contour Matching¡¨. From the results of experiment, it can meet the expectation and gain good performance and robustness.

Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was separated as salient-roads, non-salient roads and crossings and extraction of these is provided by using Ribbon Snake and Ziplock Snake methods. These methods are derived from traditional snake model. Finally, various experimental results were presented. Ribbon and Ziplock Snake methods were compared for both salient and non-salient roads. Also these methods were used to extract roads in an image. While Ribbon snake is described for extraction of salient roads in an image, Ziplock snake is applied for extraction of non-salient roads. Beside these, some constant variables in literature were redefined and expressed in a formula as depending on snake approach and a new approach for extraction of crossroads were described and tried.

Many practical applications in the field of medical image processing require valid and reliable segmentation of images. In this dissertation, we propose three different semi-automatic segmentation frameworks for 2D-upper torso medical images to construct 3D geometric model of the torso structures. In the first framework, an extended version of the Otsu&rsquo / s method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished by first using Extended Otsu&rsquo / s method and then labeling in each consecutive slice. Since there is no information about pixel positions in the outcome of Extended Otsu&rsquo / s method, we perform some processing after labeling to connect pixels belonging with the same tissue. In the second framework, Chan-Vese (CV) method, which is an example of active contour models, and a recursive connected component algorithm are used together. The segmentation process is achieved using CV method without egde information as stopping criteria. In the third and last framework, the combination of watershed transformation and K-means are used as the segmentation method. After segmentation operation, the labeling is performed for the determination of the medical structures. In addition, segmentation and labeling operation is realized for each consecutive slice in each framework. The results of each framework are compared quantitatively with manual segmentation results to evaluate their performances.

Honeybees have a rich genetic diversity in Anatolia. This is reflected in the presence of numerous subspecies of honeybee in Turkey. In METU, Department of Biology, honeybee populations of different regions in Turkey are investigated in order to characterize population variation in these regions. A total of 23 length and angle features belonging to the honeybee hindlegs and forewings are measured in these studies using a microscope and a monitor. These measurements are carried out by placing rulers on the monitor that shows the honeybee image and getting the length and angle features. However, performing measurements in this way is a time consuming process and is open to human-dependent errors. In this thesis, a &ldquo / semi-automated honeybee feature extraction system&rdquo / is presented. The aim is to increase the efficiency by decreasing the time spent on handling these measurements and by increasing the accuracy of measured hindleg and forewing features. The problem is studied from the acquisition of the microscope images, to the feature extraction of the honeybee features. In this scope, suitable methods are developed for segmentation of honeybee hindleg and forewing images. Within intermediate steps, blob analysis is utilized, and edges of the forewing and hindlegs are thinned using skeletonization. Templates that represent the forewing and hindleg edges are formed by either Bezier Curves or Polynomial Interpolation. In the feature extraction phase, Active Contour (Snake) algorithm is applied to the images in order to find the critical points using these templates.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Sistemas computacionais vÃm desempenhando papel importante em vÃrias Ãreas da medicina, notadamente no auxÃlio ao diagnÃstico mÃdico por imagem. Neste sentido, estudos na Ãrea de VisÃo Computacional sÃo realizados para desenvolver tÃcnicas e sistemas capazes de detectar automaticamente diversas doenÃas. Dentre os exames existentes que permitem o auxÃlio ao diagnÃstico e a aplicaÃÃo de sistemas computacionais em conjunto, destaca-se a Tomografia Computadorizada (TC) que possibilita a visualizaÃÃo de ÃrgÃos internos, como por exemplo, o pulmÃo e suas estruturas. Sistemas de Vis~ao Computacional utilizam estas imagens obtidas por exames de TC para extrair informaÃÃo por meio de tÃcnicas com a finalidade de segmentar, reconhecer e identificar detalhes da regiÃo de interesse nestas imagens. Este trabalho centraliza seus esforÃos na etapa de segmentaÃÃo dos pulmÃes a partir de imagens de TC, empregando-se, para tanto, tÃcnicas baseadas em MÃtodo de Contorno Ativo (MCA), tambÃm conhecido como emph{snake}. Este mÃtodo consiste em traÃar uma curva inicial, em torno ou dentro de um objeto de interesse, deformando-a conforme algumas forÃas que atuam sobre a mesma, deslocando-a atà as bordas do objeto. Este processo à realizado por iteraÃÃes sucessivas de minimizaÃÃo de uma dada funÃÃo energia, associada à curva. Neste contexto, esta tese propÃe um novo mÃtodo para a segmentaÃÃo dos pulmÃes em imagens de TC do tÃrax denominado MÃtodo de Contorno Ativo Crisp Adaptativo. Este MCA à o aperfeiÃoamento do MCA Crisp desenvolvido em um estudo anterior, que visa aumentar a precisÃo, diminuir o tempo de anÃlise e reduzir a subjetividade na segmentaÃÃo e anÃlise dos pulmÃes dessas imagens pelos mÃdicos especialistas. Este mÃtodo à proposto para a segmentaÃÃo de uma imagem isolada ou do exame completo, sendo primeiramente em 2D e expandido para 3D. O MCA Crisp Adaptativo 2D à comparado com os MCAs THRMulti, THRMod, GVF, VFC, Crisp e tambÃm com o sistema SISDEP, sendo esta avaliaÃÃo realizada utilizando como referÃncia 36 imagens segmentadas manualmente por um pneumologista. Jà o MCA Crisp Adaptativo 3D à aplicado na segmentaÃÃo dos pulmÃes em exames de TC e comparado com o mÃtodo Crescimento de RegiÃes 3D, cujos resultados das segmentaÃÃes sÃo avaliados por 2 mÃdicos pneumologistas. Os resultados obtidos demonstram que os mÃtodos propostos sÃo superiores aos demais na segmentaÃÃo dos pulmÃes em imagens de TC do tÃrax, tanto em uma imagem pelo MCA Crisp Adaptativo 2D, como em exames completos pelo MCA Crisp Adaptativo 3D. Deste modo, pode-se concluir que estes mÃtodos podem integrar sistemas de auxÃlio ao diagnÃstico mÃdico na Ãrea de Pneumologia. / Computer systems have been playing a very important role in many areas of medicine, particularly, on medical diagnosis through image processing. Therefore, studies on the field of Computer Vision are made to develop techniques and systems to perform automatic detection of several diseases. Among the existing tests that enable the diagnosis and the application of computational system together, there is the Computed Tomography (CT), which allows the visualization of internal organs, such as the lung and its structures. Image analysis techniques applied to CT scans are able to extract important information to segment and recognize details on regions of interest on these images. This work focuses its e&#8629;orts on the stage of lungs segmentation through CT images, using techniques based on Active Contour Method (ACM), also known as snake. This method consists in tracing an initial curve, around or inside the object of interest, wich deform itself according to forces that act over the same, shifting to the object edge. This process is performed by successive iterations of minimization of a given energy, associated to the curve. In this context, this work proposes a new aproach for lung segmentation of chest CT images, which is called Adaptative Crisp Active Contour Method. This ACM is an improvement the previous developed Crisp ACM. The purpose of this new ACM is to increase accuracy, decrease analysis time and reduce segmentation subjectivity in the manual analysis of specialized doctors. This method is proposed to isolated images segmentation or the complete exam, being first in 2D, then expanding to 3D. The 2D Adaptative Crisp ACM is compared to ACMs THRMulti, THRMod, GVF, VFC, Crisp and also with the system SISDEP, being this evaluation performed by using a set of 36 manually segmented images by one pulmonologist. The 3D Adaptative Crisp ACM is applied on lung segmentation in CT exams and compared with the 3D Region Growing method, which segmentation results were evaluated by two pulmonologists. The obtained results shows that the proposed methods are superior to the other methods on lung segmentation in chest CT images, both as in one image by 2D Adaptative Crisp ACM as in full exam by the 3D Adaptative Crisp ACM. Thus, it is possible to conclude that these method can integrate systems to aid medical diagnosis in the field of pulmonology.