Guest Editorial : Special issue on advanced computing for image-guided intervention

Editorial Guest Editorial: Special issue on advanced computing for image-guided intervention In the past years, we have witnessed a growing number of applications of minimally invasive or non-invasive interventions in clinical practice, where imaging is playing an essential role for the success of both diagnosis and therapy. Particularly, advanced signal and image processing algorithms are receiving increasing attention, which aim to provide accurate and reliable information directly to physicians. We have seen the applications of these technologies during all stages of an intervention, including preoperational planning, intra-operational guidance and post-operational verification. For this special issue, we have received a significant number of submissions from both academia and industry, out of which we have carefully selected eleven articles with outstanding quality. These articles have covered the topics of anatomic structure identification and tracking, image registration, data visualization and newly emerging applications. In [1] have addressed the image registration problem between preand post-radiated MRI to facilitate the evaluation of the therapeutic response after External Beam Radiation Treatment (EBRT) for the prostate cancer. A different approach has been employed by We have also included three papers on ultrasound-guided image interventions. In We have included in this special issue two papers on tissue characterization from endoscopic images. Nawarathna et al. have proposed in With the increasing use of various imaging modalities in image-guided intervention and therapy, how to optimally present and visualize the data becomes also an important issue. In [10], the authors have addressed the use of autostereoscopic volumetric visualization of the patient's anatomy, which has the potential to be combined with augmented reality. The paper especially addresses the latency problem in the visualization chain, and a few improvements have been proposed. A new adjacent application has been presented in In summary, we have seen from submissions to this special issue a growing interest in applying advanced signal and image processing technologies to image-guided interventions. The submissions have covered a wide range of clinical applications using various imaging modalities. Image feature extraction remains to be an important subject and it has to be specifically designed to suit the needs for specific applications. Learning-based approaches have also attracted a lot of attention, especially in applications requiring automatic tissue characterization and classification. We are also very happy to have received new emerging applications which are able to extend the traditional interventional imaging into greater application areas. Acknowledgments We would like to thank all the reviewers who have helped to peer-review the submitted papers and their constructive comments are well appreciated

A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application

MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ

Meme kanseri, akciğer kanserinden sonra kadınlarda kanser ölümlerinin ikinci önemli sebebidir. Erken tanı, meme kanseri tedavisinde oldukça önemlidir. Mamografi, meme kanserinin erken teşhisinde en çok kullanılan görüntüleme tekniğidir. Yapılan araştırmalar, 50 yaşın üstünde düzenli mamografi çektirmenin kadınlar için ölüm oranını %30 oranında azaltabileceğini göstermektedir. Ancak, mamogramların yorumlanması genellikle özneldir.Bu çalışmada, göğüs kitlelerinin otomatik tespiti, sınıflandırılması ve içerik tabanlı erişimi için entegre bir sistem sunulmuştur. Bu kapsamda, hekimlerin kitle hakkındaki kararları, üst düzey derin öznitelikler ve düşük seviye öznitelik seti ile ifade edilmiştir. Önerilen sistemde düşük seviyeli öznitelikleri elde etmek için, kitle tespitinde graf tabanlı görsel çıkıntı yöntemi kullanılmış ve öznitelik çıkarımı için örneklemesiz contourlet dönüşümü ve eig(Hess)-HOG yöntemleri kullanılmıştır. Ayrıca, yüksek seviyeli evrişimsel sinir ağı öznitelikleri kullanılmıştır. Ardından, test görüntülerinin kategorisini tahmin etmek için yukarıda bahsedilen özniteliklere dayalı iki aşırı öğrenme makinesi (AÖM) sınıflandırıcısı kullanılmıştır. Farklı özniteliklere dayalı sınıflandırıcıların sonuçları, test görüntülerinin türünü belirlemek için analiz edilmiştir. Görüntü erişimi ve sınıflandırma performansları, hem kesinlik-duyarlılık hem de sınıflandırma doğrulukları kullanarak IRMA mammographic patches veri setinde değerlendirilip ve karşılaştırılmıştır. Deneysel sonuçlar, önerilen sistemin etkililiğini ve gerçek zamanlı klinik uygulamalardaki kullanılabilirliğini göstermektedir