Im Rahmen der szintigraphischen Untersuchung der Halswirbelsäule gibt es unterschiedliche Aussagen zum physiologischen Speicherungsverhalten, insbesondere der kaudalen Facettengelenke. Eine Objektivierung der Szintigramme und Ermittlung von Normalbereichen der entsprechenden Speicherquotienten ist daher wichtig und stellt das Ziel dieser Arbeit dar. Zur Untersuchung kamen dafür 31 Pferde, bei denen es sich um Patienten der Chirurgischen Tierklinik in Leipzig aus dem Jahr 2008 handelte. Falls bei einem Pferd eine Lahmheit der Vordergliedmaße vorhanden war, wurde mit Hilfe der klinischen und szintigraphischen Untersuchung, sowie mittels diagnostischer Anästhesien als deren Ursache die Halswirbelsäule ausgeschlossen. Alle Pferde wiesen eine freie Beweglichkeit des Halses in alle Richtungen auf. Zur Bildung von Speicherquotienten wurden die als Interessenareale dienenden Facettengelenke C3/C4 bis C7/Th1, sowie der Wirbelkörper des sechsten Halswirbels zu verschiedenen Referenzarealen ins Verhältnis gesetzt. Als Referenzareale wurden dabei der Wirbelkörper des dritten und des vierten Halswirbels, sowie das auch als Interessenareal dienende Facettengelenk C3/C4 getestet. Anschließend wurden Normalbereiche für die Speicherquotienten ermittelt. Nach sonographischer Muskeldickenmessung über den Facettengelenken wurden deren Speicherquotienten mit Hilfe einer Formel auf einen Nullwert korrigiert, und für diese korrigierten Werte wurden ebenfalls Normalbereiche ermittelt. Es zeigte sich, dass die Speicherquotienten nach der Muskeldickenkorrektur gegenüber den nativen Speicherquotienten eine größere Streuung aufwiesen und somit größere und ungenauere Normalbereiche hervorbrachten. Dementsprechend sollten die nativen Speicherquotienten bevorzugt werden. Als das am besten geeignete Referenzareal für die Interessenareale C4/C5 bis C7/Th1 erweist sich hierbei die Isokontur-ROI auf dem Facettengelenk C3/C4. Für das Interessenareal C3/C4 eignet sich sowohl der Vergleich mit dem Referenzareal C3, als auch der mit dem Referenzareal C4. Das Interessenareal auf dem Wirbelkörper C6 wird am besten zum Referenzareal C4 ins Verhältnis gesetzt. Hervorzuheben sind die nativen Werte der Normalbereiche für die Gelenke C5/C6 und C6/C7, da hier am häufigsten röntgenologische Veränderungen zu finden sind. Sie betragen für das Gelenk C5/C6 auf der linken Halsseite 0,82-1,10 und auf der rechten Halsseite 0,86-1,10. Für das Gelenk C6/C7 beträgt der Normalbereich für die linke Halsseite 0,75-1,23 und für die rechte Halsseite 0,81-1,17. Zusammenfassend ist zu sagen, dass die quantitative Auswertung mittels der „Regions of Interest“-Technik an der Halswirbelsäule durchaus möglich ist und mit dieser Arbeit akzeptable Normalbereiche für die Facettengelenke C3/C4 bis C7/Th1 und für den Wirbelkörper C6 ermittelt werden konnten. Es fehlen nun noch Werte von Pferden mit einer klinischen Halswirbelsäulenproblematik, um die Aussagekraft der hier ermittelten Normalbereiche zu überprüfen.

The early detection of prostate cancer plays a significant role in the success of treatment and outcome. To detect prostate cancer, imaging modalities such as TransRectal UltraSound (TRUS) and Magnetic Resonance Imaging (MRI) are relied on. MRI images are more comprehensible than TRUS images which are corrupted by noise such as speckles and shadowing. However, MRI screening is costly, often unavailable in many community hospitals, time consuming, and requires more patient preparation time. Therefore, TRUS is more popular for screening and biopsy guidance for prostate cancer. For these reasons, TRUS images are chosen in this research. Radiologists first segment the prostate image from ultrasound image and then identify the hypoechoic regions which are more likely to exhibit cancer and should be considered for biopsy. In this thesis, the focus is on prostate segmentation and on Regions of Interest (ROI)segmentation. First, the extraneous tissues surrounding the prostate gland are eliminated. Consequently, the process of detecting the cancerous regions is focused on the prostate gland only. Thus, the diagnosing process is significantly shortened. Also, segmentation techniques such as thresholding, region growing, classification, clustering, Markov random field models, artificial neural networks (ANNs), atlas-guided, and deformable models are investigated. In this dissertation, the deformable model technique is selected because it is capable of segmenting difficult images such as ultrasound images. Deformable models are classified as either parametric or geometric deformable models. For the prostate segmentation, one of the parametric deformable models, Gradient Vector Flow (GVF) deformable contour, is adopted because it is capable of segmenting the prostate gland, even if the initial contour is not close to the prostate boundary. The manual segmentation of ultrasound images not only consumes much time and effort, but also leads to operator-dependent results. Therefore, a fully automatic prostate segmentation algorithm is proposed based on knowledge-based rules. The new algorithm results are evaluated with respect to their manual outlining by using distance-based and area-based metrics. Also, the novel technique is compared with two well-known semi-automatic algorithms to illustrate its superiority. With hypothesis testing, the proposed algorithm is statistically superior to the other two algorithms. The newly developed algorithm is operator-independent and capable of accurately segmenting a prostate gland with any shape and orientation from the ultrasound image. The focus of the second part of the research is to locate the regions which are more prone to cancer. Although the parametric dynamic contour technique can readily segment a single region, it is not conducive for segmenting multiple regions, as required in the regions of interest (ROI) segmentation part. Since the number of regions is not known beforehand, the problem is stated as 3D one by using level set approach to handle the topology changes such as splitting and merging the contours. For the proposed ROI segmentation algorithm, one of the geometric deformable models, active contours without edges, is used. This technique is capable of segmenting the regions with either weak edges, or even, no edges at all. The results of the proposed ROI segmentation algorithm are compared with those of the two experts' manual marking. The results are also compared with the common regions manually marked by both experts and with the total regions marked by either expert. The proposed ROI segmentation algorithm is also evaluated by using region-based and pixel-based strategies. The evaluation results indicate that the proposed algorithm produces similar results to those of the experts' manual markings, but with the added advantages of being fast and reliable. This novel algorithm also detects some regions that have been missed by one expert but confirmed by the other. In conclusion, the two newly devised algorithms can assist experts in segmenting the prostate image and detecting the suspicious abnormal regions that should be considered for biopsy. This leads to the reduction the number of biopsies, early detection of the diseased regions, proper management, and possible reduction of death related to prostate cancer.

The early detection of prostate cancer plays a significant role in the success of treatment and outcome. To detect prostate cancer, imaging modalities such as TransRectal UltraSound (TRUS) and Magnetic Resonance Imaging (MRI) are relied on. MRI images are more comprehensible than TRUS images which are corrupted by noise such as speckles and shadowing. However, MRI screening is costly, often unavailable in many community hospitals, time consuming, and requires more patient preparation time. Therefore, TRUS is more popular for screening and biopsy guidance for prostate cancer. For these reasons, TRUS images are chosen in this research. Radiologists first segment the prostate image from ultrasound image and then identify the hypoechoic regions which are more likely to exhibit cancer and should be considered for biopsy. In this thesis, the focus is on prostate segmentation and on Regions of Interest (ROI)segmentation. First, the extraneous tissues surrounding the prostate gland are eliminated. Consequently, the process of detecting the cancerous regions is focused on the prostate gland only. Thus, the diagnosing process is significantly shortened. Also, segmentation techniques such as thresholding, region growing, classification, clustering, Markov random field models, artificial neural networks (ANNs), atlas-guided, and deformable models are investigated. In this dissertation, the deformable model technique is selected because it is capable of segmenting difficult images such as ultrasound images. Deformable models are classified as either parametric or geometric deformable models. For the prostate segmentation, one of the parametric deformable models, Gradient Vector Flow (GVF) deformable contour, is adopted because it is capable of segmenting the prostate gland, even if the initial contour is not close to the prostate boundary. The manual segmentation of ultrasound images not only consumes much time and effort, but also leads to operator-dependent results. Therefore, a fully automatic prostate segmentation algorithm is proposed based on knowledge-based rules. The new algorithm results are evaluated with respect to their manual outlining by using distance-based and area-based metrics. Also, the novel technique is compared with two well-known semi-automatic algorithms to illustrate its superiority. With hypothesis testing, the proposed algorithm is statistically superior to the other two algorithms. The newly developed algorithm is operator-independent and capable of accurately segmenting a prostate gland with any shape and orientation from the ultrasound image. The focus of the second part of the research is to locate the regions which are more prone to cancer. Although the parametric dynamic contour technique can readily segment a single region, it is not conducive for segmenting multiple regions, as required in the regions of interest (ROI) segmentation part. Since the number of regions is not known beforehand, the problem is stated as 3D one by using level set approach to handle the topology changes such as splitting and merging the contours. For the proposed ROI segmentation algorithm, one of the geometric deformable models, active contours without edges, is used. This technique is capable of segmenting the regions with either weak edges, or even, no edges at all. The results of the proposed ROI segmentation algorithm are compared with those of the two experts' manual marking. The results are also compared with the common regions manually marked by both experts and with the total regions marked by either expert. The proposed ROI segmentation algorithm is also evaluated by using region-based and pixel-based strategies. The evaluation results indicate that the proposed algorithm produces similar results to those of the experts' manual markings, but with the added advantages of being fast and reliable. This novel algorithm also detects some regions that have been missed by one expert but confirmed by the other. In conclusion, the two newly devised algorithms can assist experts in segmenting the prostate image and detecting the suspicious abnormal regions that should be considered for biopsy. This leads to the reduction the number of biopsies, early detection of the diseased regions, proper management, and possible reduction of death related to prostate cancer.

Yes / Introduction : Rheumatoid arthritis (RA) affects 1% of the population and is principally associated with joint inflammation. It is suggested however that muscle involvement may be one of the earliest clinical features of RA. It is therefore important that techniques exist to accurately assess muscle health in those with RA to enable successful treatment. This study assesses the inter-rater and intra-rater repeatability of Diffusion Tensor MRI (DTI), 2-Point Dixon fat fraction, and T2 relaxation of the thigh muscle in patients with RA using manual regions of interest (ROI). Methods: Nineteen patients (10/19 males; mean age 59; range 18-85) diagnosed with RA had an MRI scan of their hamstrings and quadriceps muscles to obtain fat fraction (FF), mean diffusivity (MD), fractional anisotropy (FA), and T2 quantitative measurements. Two raters (R#1 & R#2) (initials removed for review) independently contoured ROIs for each patient. R#1 repeated the ROI for the same 19 patients after a 6-month hiatus to assess intra-rater repeatability. Inter-rater and intra-rater repeatability for the ROI measurements were compared using Inter Class Correlation (ICC) and Bland-Altman plots. Results: There was excellent agreement for both inter-rater and intra-rater repeatability. ICC results ranged from 0.900-0.998 (P<0.001), and intra-rater ICC results ranged from 0.977-0.999 (P<0.001). Bland-Altman plots also showed excellent agreement. Conclusions: ICC measurements and Bland-Altman plots showed excellent repeatability and agreement with no statistically significant differences when assessing the inter-rater and intra-rater repeatability of FF, MD, FA, and T2 relaxation of the thigh muscle using manual regions of interest in patients with RA. Implications for practice: Manual ROI drawing does not introduce significant errors obtaining FF, MD, FA, and T2 MRI measurements in an RA population. / This research is funded by the NIHR infrastructure at Leeds.

Im Rahmen der szintigraphischen Untersuchung der Halswirbelsäule gibt es unterschiedliche Aussagen zum physiologischen Speicherungsverhalten, insbesondere der kaudalen Facettengelenke. Eine Objektivierung der Szintigramme und Ermittlung von Normalbereichen der entsprechenden Speicherquotienten ist daher wichtig und stellt das Ziel dieser Arbeit dar. Zur Untersuchung kamen dafür 31 Pferde, bei denen es sich um Patienten der Chirurgischen Tierklinik in Leipzig aus dem Jahr 2008 handelte. Falls bei einem Pferd eine Lahmheit der Vordergliedmaße vorhanden war, wurde mit Hilfe der klinischen und szintigraphischen Untersuchung, sowie mittels diagnostischer Anästhesien als deren Ursache die Halswirbelsäule ausgeschlossen. Alle Pferde wiesen eine freie Beweglichkeit des Halses in alle Richtungen auf. Zur Bildung von Speicherquotienten wurden die als Interessenareale dienenden Facettengelenke C3/C4 bis C7/Th1, sowie der Wirbelkörper des sechsten Halswirbels zu verschiedenen Referenzarealen ins Verhältnis gesetzt. Als Referenzareale wurden dabei der Wirbelkörper des dritten und des vierten Halswirbels, sowie das auch als Interessenareal dienende Facettengelenk C3/C4 getestet. Anschließend wurden Normalbereiche für die Speicherquotienten ermittelt. Nach sonographischer Muskeldickenmessung über den Facettengelenken wurden deren Speicherquotienten mit Hilfe einer Formel auf einen Nullwert korrigiert, und für diese korrigierten Werte wurden ebenfalls Normalbereiche ermittelt. Es zeigte sich, dass die Speicherquotienten nach der Muskeldickenkorrektur gegenüber den nativen Speicherquotienten eine größere Streuung aufwiesen und somit größere und ungenauere Normalbereiche hervorbrachten. Dementsprechend sollten die nativen Speicherquotienten bevorzugt werden. Als das am besten geeignete Referenzareal für die Interessenareale C4/C5 bis C7/Th1 erweist sich hierbei die Isokontur-ROI auf dem Facettengelenk C3/C4. Für das Interessenareal C3/C4 eignet sich sowohl der Vergleich mit dem Referenzareal C3, als auch der mit dem Referenzareal C4. Das Interessenareal auf dem Wirbelkörper C6 wird am besten zum Referenzareal C4 ins Verhältnis gesetzt. Hervorzuheben sind die nativen Werte der Normalbereiche für die Gelenke C5/C6 und C6/C7, da hier am häufigsten röntgenologische Veränderungen zu finden sind. Sie betragen für das Gelenk C5/C6 auf der linken Halsseite 0,82-1,10 und auf der rechten Halsseite 0,86-1,10. Für das Gelenk C6/C7 beträgt der Normalbereich für die linke Halsseite 0,75-1,23 und für die rechte Halsseite 0,81-1,17. Zusammenfassend ist zu sagen, dass die quantitative Auswertung mittels der „Regions of Interest“-Technik an der Halswirbelsäule durchaus möglich ist und mit dieser Arbeit akzeptable Normalbereiche für die Facettengelenke C3/C4 bis C7/Th1 und für den Wirbelkörper C6 ermittelt werden konnten. Es fehlen nun noch Werte von Pferden mit einer klinischen Halswirbelsäulenproblematik, um die Aussagekraft der hier ermittelten Normalbereiche zu überprüfen.

Modern medicine has become reliant on medical imaging. Multiple modalities, e. g. magnetic resonance imaging (MRI), computed tomography (CT), etc. , are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. <br /><br /> In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions. <br /><br /> This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions. <br /><br /> We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images. <br /><br /> Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures. <br /><br /> Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.

Nowadays, there exist a huge number of end devices with different screen properties for watching television content, which is either broadcasted or transmitted over the internet. To allow best viewing conditions on each of these devices, different image formats have to be provided by the broadcaster. Producing content for every single format is, however, not applicable by the broadcaster as it is much too laborious and costly. The most obvious solution for providing multiple image formats is to produce one high resolution format and prepare formats of lower resolution from this. One possibility to do this is to simply scale video images to the resolution of the target image format. Two significant drawbacks are the loss of image details through ownscaling and possibly unused image areas due to letter- or pillarboxes. A preferable solution is to find the contextual most important region in the high-resolution format at first and crop this area with an aspect ratio of the target image format afterwards. On the other hand, defining the contextual most important region manually is very time consuming. Trying to apply that to live productions would be nearly impossible. Therefore, some approaches exist that automatically define cropping areas. To do so, they extract visual features, like moving reas in a video, and define regions of interest (ROIs) based on those. ROIs are finally used to define an enclosing cropping area. The extraction of features is done without any knowledge about the type of content. Hence, these approaches are not able to distinguish between features that might be important in a given context and those that are not. The work presented within this thesis tackles the problem of extracting visual features based on prior knowledge about the content. Such knowledge is fed into the system in form of metadata that is available from TV production environments. Based on the extracted features, ROIs are then defined and filtered dependent on the analysed content. As proof-of-concept, this application finally adapts SDTV (Standard Definition Television) sports productions automatically to image formats with lower resolution through intelligent cropping and scaling. If no content information is available, the system can still be applied on any type of content through a default mode. The presented approach is based on the principle of a plug-in system. Each plug-in represents a method for analysing video content information, either on a low level by extracting image features or on a higher level by processing extracted ROIs. The combination of plug-ins is determined by the incoming descriptive production metadata and hence can be adapted to each type of sport individually. The application has been comprehensively evaluated by comparing the results of the system against alternative cropping methods. This evaluation utilised videos which were manually cropped by a professional video editor, statically cropped videos and simply scaled, non-cropped videos. In addition to and apart from purely subjective evaluations, the gaze positions of subjects watching sports videos have been measured and compared to the regions of interest positions extracted by the system.

Modern medicine has become reliant on medical imaging. Multiple modalities, e. g. magnetic resonance imaging (MRI), computed tomography (CT), etc. , are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. <br /><br /> In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions. <br /><br /> This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions. <br /><br /> We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images. <br /><br /> Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures. <br /><br /> Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.

No / In this paper we present a new method for accurate real-time facial feature detection. Our method is based on local feature detection and enhancement. Previous work in this area, such as that of Viola and Jones, require looking at the face as a whole. Consequently, such approaches have increased chances of reporting negative hits. Furthermore, such algorithms require greater processing power and hence they are especially not attractive for real-time applications. Through our recent work, we have devised a method to identify the face from real-time images and divide it into regions of interest (ROI). Firstly, based on a face detection algorithm, we identify the face and divide it into four main regions. Then, we undertake a local search within those ROI, looking for specific facial features. This enables us to locate the desired facial features more efficiently and accurately. We have tested our approach using the Cohn-Kanade’s Extended Facial Expression (CK+) database. The results show that applying the ROI has a relatively low false positive rate as well as provides a marked gain in the overall computational efficiency. In particular, we show that our method has a 4-fold increase in accuracy when compared to existing algorithms for facial feature detection.

No / The purpose of this chapter is to discuss and present a computational framework for detecting and analysing facial expressions efficiently. The approach here is to identify the face and estimate regions of facial features of interest using the optical flow algorithm. Once the regions and their dynamics are computed a rule based system can be utilised for classification. Using this framework, we show how it is possible to accurately identify and classify facial expressions to match with FACS coding and to infer the underlying basic emotions in real time.