Methodological aspects for improving clinical value of SPECT and MRI

Image processing methods were developed for SPECT and MR images. The methods were validated in clinical environment. Segmentation of SPECT images for region of interest (ROI) analysis was found to be unreliable without accurate attenuation and scatter correction for the original images. The reliability of ROI analysis of brain SPECT images was enhanced using registration with MRI. The method was based on external markers. The registration error was studied using phantom tests and simulations. It was concluded that the registration accuracy was not the limiting factor in ROI analysis of the registered images provided that the external marker system was properly designed and attached. Quality requirements for MRI data from patients with cerebral infarctions were evaluated in order to make segmentation as automatic as possible. Quantitative information from these images could be extracted with e.g. statistical and neural network classifiers, but required more manual work than expected due to the visible intensity nonuniformity in the images. The third application consisted of developing a registration methodology for ictal and interictal SPECT, MRI and EEG for improved localization of the epileptogenic foci. The methodology was based on SPECT transmission imaging. The accuracy of registration was about 3-5 mm. As a conclusion, improved analysis of SPECT and MR images was obtained with the carefully evaluated methodology presented in the thesis. The registration procedure for brain SPECT and MRI as well as the registration procedure for epilepsy surgery candidates are in clinical use for selected patients in Helsinki University Central Hospital (currently Health Care Region of Helsinki and Uusimaa).reviewe

Automatic caption generation for content-based image information retrieval.

Ma, Ka Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 82-87).Abstract and appendix in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objective of This Research --- p.4Chapter 1.2 --- Organization of This Thesis --- p.5Chapter 2 --- Background --- p.6Chapter 2.1 --- Textual - Image Query Approach --- p.7Chapter 2.1.1 --- Yahoo! Image Surfer --- p.7Chapter 2.1.2 --- QBIC (Query By Image Content) --- p.8Chapter 2.2 --- Feature-based Approach --- p.9Chapter 2.2.1 --- Texture Thesaurus for Aerial Photos --- p.9Chapter 2.3 --- Caption-aided Approach --- p.10Chapter 2.3.1 --- PICTION (Picture and capTION) --- p.10Chapter 2.3.2 --- MARIE --- p.11Chapter 2.4 --- Summary --- p.11Chapter 3 --- Caption Generation --- p.13Chapter 3.1 --- System Architecture --- p.13Chapter 3.2 --- Domain Pool --- p.15Chapter 3.3 --- Image Feature Extraction --- p.16Chapter 3.3.1 --- Preprocessing --- p.16Chapter 3.3.2 --- Image Segmentation --- p.17Chapter 3.4 --- Classification --- p.24Chapter 3.4.1 --- Self-Organizing Map (SOM) --- p.26Chapter 3.4.2 --- Learning Vector Quantization (LVQ) --- p.28Chapter 3.4.3 --- Output of the Classification --- p.30Chapter 3.5 --- Caption Generation --- p.30Chapter 3.5.1 --- Phase One: Logical Form Generation --- p.31Chapter 3.5.2 --- Phase Two: Simplification --- p.32Chapter 3.5.3 --- Phase Three: Captioning --- p.33Chapter 3.6 --- Summary --- p.35Chapter 4 --- Query Examples --- p.37Chapter 4.1 --- Query Types --- p.37Chapter 4.1.1 --- Non-content-based Retrieval --- p.38Chapter 4.1.2 --- Content-based Retrieval --- p.38Chapter 4.2 --- Hierarchy Graph --- p.41Chapter 4.3 --- Matching --- p.42Chapter 4.4 --- Summary --- p.48Chapter 5 --- Evaluation --- p.49Chapter 5.1 --- Experimental Set-up --- p.50Chapter 5.2 --- Experimental Results --- p.51Chapter 5.2.1 --- Segmentation --- p.51Chapter 5.2.2 --- Classification --- p.53Chapter 5.2.3 --- Captioning --- p.55Chapter 5.2.4 --- Overall Performance --- p.56Chapter 5.3 --- Observations --- p.57Chapter 5.4 --- Summary --- p.58Chapter 6 --- Another Application --- p.59Chapter 6.1 --- Police Force Crimes Investigation --- p.59Chapter 6.1.1 --- Image Feature Extraction --- p.61Chapter 6.1.2 --- Caption Generation --- p.64Chapter 6.1.3 --- Query --- p.66Chapter 6.2 --- An Illustrative Example --- p.68Chapter 6.3 --- Summary --- p.72Chapter 7 --- Conclusions --- p.74Chapter 7.1 --- Contribution --- p.77Chapter 7.2 --- Future Work --- p.78Bibliography --- p.81Appendices --- p.88Chapter A --- Segmentation Result Under Different Parametes --- p.89Chapter B --- Segmentation Time of 10 Randomly Selected Images --- p.90Chapter C --- Sample Captions --- p.9

Kemik metastazlarının görüntü işleme ve yapay sinir ağları yöntemleri ile tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dünyada yapılan istatistikler, kanser ve kansere bağlı ölüm vakalarının 20 yıl içerisinde ciddi oranlarda artacağını göstermektedir. Birçok kanser türü için erken teşhis; daha kolay bir tedavi ve daha yüksek bir başarı ihtimali anlamına gelmektedir. Hekimler/Radyologlar görüntüleme yöntemleri ile elde edilen görüntüleri yorumlayarak erken dönemde kanser vakalarını teşhis etmeye çalışmaktalar. Ancak, artan kanser vakalarına kıyasla hekimlerin sayısı ülkemizde olduğu gibi tüm dünya genelinde sınırlı kalmaktadır. Bu da beraberinde hekimler için artan bir iş yükü demektir. İncelenecek vaka sayısının artması da beraberinde hatalı teşhis oranlarının artmasına yol açabilmektedir. Son yıllarda, bu gibi dezavantajları giderebilecek Bilgisayar Destekli Tespit (BDT) yöntemleri giderek önemli olmaya başlamıştır. Radyolojik görüntüler üzerinde şüpheli durumları daha belirgin hale getirerek hekimleri uyaran ve otomatik hastalık teşhisi yapan destek karar sistemleri hekimlerin hem hata oranlarını düşürmekte hem de daha az eforla teşhis gerçekleştirmelerine katkıda bulunmaktadır. Bu tez çalışmasında, iskelet sistemi metastazlarının kemik sintigrafisi görüntülerinde otomatik olarak tespitine imkan sağlayan bir BDT sistemi geliştirilmiştir. Önerilen BDT sistemi, sintigrafi görüntülerini giriş olarak almakta ve bu görüntüler üzerindeki artan tutulum alanlarını işaretleyerek metastaz olup olmadığına karar vermektedir. Görüntülerdeki tutulumların hepsi kanser veya metastaz anlamına gelmemektedir. Mesane, dizler, dirsekler ve kafatasının bazı bölgelerinde kanser olmadığı halde artan tutulumlar gibi gözükmektedir. Bu sebeple, önerilen yöntem bu gibi durumlar ile gerçek tutulumları doğru bir şekilde ayırabilmek için görüntü işleme ve örüntü tanıma tekniklerinden faydalanmaktadır. Öncelikle, alınan sintigrafi resimleri ön-işlem aşamalarından geçirilerek tutulum alanları daha belirgin hale getirilmekte daha sonra ise tutulum alanlarını tespit etmek için bölütleme yöntemi kullanılmaktadır. Çalışmada hangi bölütleme yönteminin kemik sintigrafilerinde artan tutulumları daha etkili bir şekilde bölütleyebileceğini araştırmak için çeşitli deneyler gerçekleştirilmiştir. 706 adet görüntü üzerinde yapılan detaylı deneylere göre seçilen Level Set Active Contour (LSAC), Self Organizing Maps (SOM) ve Fuzzy C-Means yöntemlerinden en başarılı bölütleme yönteminin LSAC olduğu tespit edilmiştir. Bunun yanında, bölütlenmiş sintigrafi görüntülerinden özellik çıkarımını gerçekleştirmek için basit olmasına karşın etkili bir özellik çıkarım yöntemi olan Görüntü Izgaralama yöntemi önerilmiştir. Yapay Sinir Ağları (YSA) kullanılarak yapılan metastaz ayırımında %92.3 doğruluk oranı, %94 duyarlılık oranı, %86,67 özgüllük oranı tespit edilebilmiştir. Böylece hekimlerin karar verme sürecine destek olacak bir ek araç geliştirilmiştirThe statistics show that cancer and cancer-related deaths will increase significantly over the next 20 years. Early detection means easier treatment and higher probability of success for many types of cancer. Physicians/radiologists are trying to diagnose cancers early by using images obtained by imaging methods. However, the number of physicians are limited compared to the increasing cases of cancer all over the world. This also means an increased workload for physicians. Rapid growth of cancer cases to be examined can lead to an increased rate of false diagnoses. In recent years, Computer Aided Detection (CAD) methods are becoming of importance to resolve such disadvantages. Decision support systems which diagnose disease automatically and warn physicians for suspicious cases in the radiological images reduce the error rates of doctors and are beneficial with regards to using less effort to diagnose. In this study, a CAD system is developed to allow the automatic diagnosis of skeletal metastasis for bone scintigraphy images. The proposed CAD system takes the scintigraphic images as input and provides a decision about suspicious areas by marking hot spots on these images. All of the hot spots found in the images do not mean cancer or metastasis. Although bladder, knees, elbows and even the some part of the skull do not have metastasis, hot spots can be seen in these parts of the body. The proposed method takes advantage of image processing and pattern recognition techniques to separate metastasis correctly. Pre-processing methods is used primarily to highlight the hot spots and then, segmentation method is performed for detection of hot spots. Various experiments were conducted to investigate the best segmentation method. LSAC was found the most successful segmentation method. Detailed experiments have been made on 706 images using Level Set Active Contour (LSAC), Self-Organizing Maps (SOM) and Fuzzy C-Means methods . Furthermore, easy but effective feature extraction method, Image Gridding, is proposed for the segmented scintigraphy images. Artificial Neural Networks (ANN) is used for classification of metastatic disease. The CAD system detected 92.3% accuracy, 94% sensitivity, 86.67% specificity. Thus, an additional tool has been developed to support the decision making process of physicians

Endoskopik ultrason görüntülerinde kronik pankreatit ve pankreas kanserinin yaşa göre bilgisayar destekli teşhisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kronik pankreatit (KP) ve pankreas kanseri (PK), pankreasın fonksiyonlarını bozan ve ölümcül olabilen rahatsızlıkların başında gelmektedir. Pankreas kanseri, kötü huylu tümörlerin pankreas bölgesinde gelişmesi sonucu ortaya çıkmaktadır. Bu tümörler çok kısa süre içerisinde büyümesine karşın diğer organlara baskı yapmadığı için hastada önemli belirtiler görülmemektedir. Çoğu durumda da belirtilerin ortaya çıkması kanserin tedavi edilemeyecek hale gelmesi anlamına gelebilmektedir. Dolayısıyla, kanserin erken dönemde teşhisi hayatta kalma oranlarının arttırılması açısından son derece önemlidir. Kronik pankreatit ise pankreasın iltihaplanma hastalığıdır ve bu rahatsızlıkta değişikliklerin ortaya çıkması uzun yıllar alabileceği için erken evrede tanınması oldukça zordur. Hastalığı normal pankreas dokusundan ve pankreas kanserinden ayırmak da son derece güç olabilmektedir. Pankreas kanserinin ve kronik pankreatitin teşhisinde, MR ve BT'ye oranla daha yüksek hassasiyete sahip olduğu için Endoskopik Ultrason (EUS) tercih edilmektedir. Ancak, uzmanlar kesin tanı için genelde biyopsiye başvurmaktadır. Fakat biyopsinin invazif bir yöntem olması ve özellikle de pankreas bölgesi için uygulanmasının zor olması daha farklı çözümlerin geliştirilmesini gerektirmektedir. Son yıllarda, yapay zeka tekniklerindeki gelişmeler, hekimlere tanıda yardımcı olacak Bilgisayar Destekli Teşhis (BDT) sistemlerinin geliştirilmesinin önünü açmış ve farklı bir çok rahatsızlığın teşhisine katkı için BDT sistemleri geliştirilmiştir. Bu tez çalışmasında, EUS görüntülerinde pankreas kanserinin ve kronik pankreatitin normal pankreas dokusundan ayrılmasını sağlayacak yarı-otomatik bir BDT sistemi geliştirilmiştir. Önerilen sistemde, literatürdekilerden farklı olarak pankreasın yaşa göre gösterdiği morfolojik değişiklikler dikkate alınarak EUS görüntüleri hastaların yaşına göre 3 ayrı gruba bölünmüştür. 40 yaşından küçük, 40 ile 60 yaş arası ve 60 yaşından büyük olarak ayrılan görüntüler ayrı ayrı sisteme öğretilmiş ve sınıflandırma ayrı bir şekilde gerçekleştirilmiştir. Böylece, daha yüksek başarım elde edilmesi hedeflenmiştir. Tasarımın ilk adımında uzman eşliğinde pankreas bölgesi işaretlenerek kesilmiştir. Sonrasında, bu bölgeden 122 adet özellik çıkarılmıştır, son olarak da Destek Vektör Makinaları (DVM) ile vakalar PK-sağlıklı, KP-sağlıklı, KP-PK olmak üzere ikili olarak sınıflandırılmıştır. Sistemin başarım değerlendirilmesi için 202 kanserli, 130 sağlıklı ve 69 pankreatitli olmak üzere toplamda 401 adet EUS görüntüsü kullanılmıştır. Gerçekleştirilen deney sonuçlarına göre önerilen sistem ile yaş grupları dikkate alındığında pankreas kanseri ile kronik pankreatit vakaları %97.95 doğruluk ile panreas kanseri ile normal pankreas ayrımı %96.15 oranında, kronik pankreatit ile normal pankreas dokusu ise %100 oranı ile teşhis edilebilmiştir. Sonuç olarak hekimlere kronik pankreatit ve pankreas kanserinin ayrımında destek olabilecek bir BDT sistemi önerilmiştir.Chronic Pancreatitis (CP) and Pancreatic Cancer (PC) are the leading diseases that impair pancreas functions and that can be fatal. PC is caused by the development of malignant tumors in pancreas region. Although these tumors grow in a very short time, important symptoms are not observable in patients since these tumors do not press on the other organs. In most cases, when symptoms occur, the cancer has already spread too far to be cured. Therefore, early detection of PC is crucial to increase the survival rate. CP is the inflammation of the pancreas and early diagnosis is difficult since it may take years for the changes to emerge. It is also extremely hard to differentiate this disease from normal pancreas tissues and pancreatic cancer as well. Endoscopic Ultrasonography (EUS) is preferred in the diagnosis of pancreatic cancer and chronic pancreatitis due to better precision compared to MR and CT. However; experts normally resort to biopsy for definitive diagnosis. Since biopsy is an invasive method and especially difficult to be implemented in the pancreas region, different solutions need to be developed in this field. Recent developments in artificial intelligence techniques have paved the way for physicians to develop Computer-aided Detection (CAD) systems and CAD systems have been developed to contribute to the diagnosis of many diseases. In this thesis, different from the studies in the literature, a semi-automatic CAD system was developed that allows separation of normal pancreatic tissues from tissues with pancreatic cancer and chronic pancreatitis. EUS images in the proposed system were divided into three groups based on patients' ages considering the morphological changes observed in the pancreas due to aging. Images divided into three groups as younger than 40, between 40 and 60 and older than 60 were taught to the system separately and classified to obtain higher performance. The first step in the design includes marking and cutting the pancreas region. Later 122 features were extracted from this region and the most important 20 features were selected. At the final stage, the cases were dyadically classified with the help of Support Vector Machines (SVM) as PC-healthy, CP-healthy and CP-PC. A total of 401 EUS images were utilized (202 cancer, 130 healthy and 69 pancreatic) for system performance. According to the results of the experiment and taking the age groups into account, the proposed system detected pancreatic cancer and chronic pancreatitis cases with 97.95% accuracy rate, differentiated between pancreatic cancer and normal pancreas with 96.15% accuracy and chronic pancreatitis and normal pancreas tissues with 100% accuracy rate. Results indicate that the study proposes a CAD system that can support physicians in differentiating between pancreatic cancer and chronic pancreatitis cases

Automatic segmentation of Nucleus Accumbens

Segmentation of subcortical structures in the brain has become an increasingly important topic in contemporary medicine. The ability to effi ciently isolate different regions of the human brain has allowed doctors and technicians to become more e fficient in the diagnosis of mental disorders and the evaluation of the patient conditions. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens, which is believed to play a central role in the reward circuit. In fact, studies of volumetric brain magnetic resonance imaging (MRI) have shown neuroanatomical abnormalities of this structure in adult attention defficit/hyperactivity disorder (ADHD), and speci cally a smaller average volume of the region. The use of a reliable automated segmentation method would therefore represent an extremely helpful and e fficient tool for identifying this disorder, especially when compared to manual volume labeling methods, which often turn out to be tedious and extremely time-consuming. However, automatic segmentation of the Accumbens is extremely di fficult to obtain, due to the lack of contrast with the surrounding structures. This means that most conventional segmentation methods are useless for this purpose, and makes the segmentation method selection a very delicate procedure. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. The research project aims to apply pre-existing segmentation methods to the Nucleus Accumbens, moving then to an evaluation of such methods and an estimation of how e ffective they are. Diff erent segmentation methods were used for this purpose; firstly, the standard Atlas Segmentation Approach was used, showing generally poor results paired with long computational times and high complexity. Moreover, this method has shown potential problems in the individuation of the correct region, leading, in some cases, to completely wrong segmentations. In addition to the fi rst method, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods have been implemented. The results have shown improved accuracy and better performance than the original method. Judging by the results, the segmentation of the Nucleus Accumbens has proven to be an extremely complicated task, both for the dimension of the structure itself and for the lack of contrast with the surrounding structures. In order to improve detection accuracy, combination of multiple methods is necessary, as using a single method for the segmentation process can lead to an incorrect labeling

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine