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"Creating new medical ontologies for image annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system, cross media relevance models. Based on a text query the system will retrieve the images that contain objects described by the keywords."

"Given the rapid evolution of radiological imaging in the past four decades, medical image processing nowadays is an essential tool for clinical research. Applications range from research in neuroscience, biomechanics and biomedical engineering to clinical routine tasks such as the visualization of huge datasets provided by modern computed tomography systems in radiology, the manufacturing of patient-specific prostheses for orthopedic surgery, the precise planning of dose distributions in radiation oncology, the fusion of multimodal image data for therapymonitoring in internal medicine, and computer-aided neurosurgical interventions."

"The book provides a gradual, down to earth introduction to DICOM, accompanied by an analysis of the most common problems associated with its implementation. "

"This book constitutes the refereed proceedings of the International Workshop on Mesh Processing in Medical Image Analysis, MeshMed 2012, held in Nice, France, in October 2012 in conjunction with MICCAI 2012, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention. The book includes 16 submissions, 8 were selected for presentation along with the 3 plenary talks representative of the meshing, and 8 were selected for poster presentations. The papers cover a broad range of topics, including statistical shape analysis and atlas construction, novel meshing approaches, soft tissue simulation, quad dominant meshing and mesh based shape descriptors. The described techniques were applied to a variety of medical data including cortical bones, ear canals, cerebral aneurysms and vascular structures."

"Dalam mendapatkan hasil pencitraan Kedokteran Nuklir yang baik adalah didukung dengan instrumen dengan performa yang baik yaitu sesuai dengan standar mutu alat tersebut. Untuk menjaga agar performa instrumen tetap bekerja secara optimal harus dilakukan uji secara berkala dengan protokol uji yang tepat. Penelitian ini dilakukan untuk menguji kualitas citra kamera gamma dengan fantom IEC 61675-2 dan membuat protokol pengujianya meliputi pengujian resolusi radial, resolusi tangensial dan scatter fraction. Pengujian resolusi radial dan resolusi tangensial dilakukan menggunakan sumber titik sedangkan untuk pengujian scatter fraction menggunakan sumber garis. Dari penelitian ini didapatkan hasil rerata FWHM untuk resolusi radial 14,08, pada resolusi tangensial 10,21 mm. Untuk pengujian scatter fraction didapatkan hasil 0,042 dengan prosentase hamburan 4,2%.

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In order to achieve an optimal scanned image of nuclear medicine device, a good instrument with proper performance which is consistent with its standard quality is required. Furthermore, suitable and periodic protocol test is also a good requirement so that the instrument can extend its maximum performance.In order to Achieve an optimal scanned image of nuclear medicine devices, a good instrument with proper roomates performance is consistent with its standard quality is required.

This research was conducted to test the image quality of gamma cameras with phantom IEC 61675-2 and make protocol includes testing radial resolution, tangential resolution and scatter fraction. Test of tangential and radial resolution made using a point source, while for test Scatter fraction using a line source. From this research, the results of the mean FWHM for the radial resolution 14.08 mm and 10.21 mm in tangential resolution. test Scatter fraction obtained scattering percentage of 4.2%."

"Penyakit mata berat yang telat tertangani seperti katarak, glaukoma, serta retinopati diabetik merupakan salah satu penyebab utama gangguan penglihatan dan kebutaan. Pencegahan dapat dilakukan dengan melakukan pendektesian dini melalui citra fundus. Untuk mengatasi minimnya dokter mata dan persebarannya yang masih belum merata, dilakukan pendektesian penyakit mata secara otomatis melalui gambar mata dengan pendekatan deep learning. Dalam penelitian ini, digunakan metode Transfer Learning U-Net dengan VGG16 sebagai pretrained model (V-Unet) yang telah dilatih pada online database, ImageNet. Data yang digunakan dalam penelitian ini merupakan data citra fundus yang diperoleh dari platform Kaggle. Preprocessing data pada citra fundus yang dilakukan untuk meningkatkan kinerja model adalah centered crop, resize, dan rescale. Fungsi optimasi Adam digunakan untuk meminimalkan fungsi loss ketika melatih model. Pada penelitian ini, dilakukan pemisahan data training, validasi, testing dengan 3 rasio berbeda, yaitu kasus I dengan rasio 60:20:20, kasus II dengan rasio 70:20:10, dan kasus III dengan rasio 80:10:10. Hasil penelitian ini menunjukkan bahwa V-Unet memiliki kinerja paling baik pada kasus II berdasarkan skor AUC dan running time dengan nilai rata-rata skor AUC 0,8622 dan rata-rata running time 3,7079 detik sedangkan berdasarkan nilai akurasinya V-Unet memiliki kinerja paling baik pada kasus III dengan rata-rata nilai akurasi sebesar 66,34%.

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Untreated severe eye diseases such as cataracts, glaucoma, and diabetic retinopathy is one of the main causes of visual impairment and blindness. Prevention can be done by doing early detection through fundus images. To overcome the lack of ophthalmologists and their uneven distribution, an automatic detection of eye diseases is carried out through eye images using a deep learning approach. In this study, Transfer Learning U-Net method was used with VGG16 as a pre-trained model (V-Unet) which had been trained on the online database, ImageNet . The data used in this study is fundus image data that obtained from the Kaggle platform. Preprocessing data on the fundus image that is carried out to improve model performance is centered crop, resize, and rescale. Adam's optimization function used to minimize the loss function when training the model. In this study, the training, validation, testing data was separated with 3 different ratios, namely case I with a ratio of 60:20:20, case II with a ratio of 70:20:10, and case III with a ratio of 80:10:10. The results of this study indicate that V-Unet has the best performance in case II based on the AUC score and running time with an average AUC score of 0.8622 and an average running time of 3.7079 seconds while based on accuracy value the best case is case III with an average accuracy value of 66.34%."

"Perkembangan terkini dari perangkat pencitraan medik computerized tomography (CT) scan telah memungkinkan dihasilkannya citra dari penampang melintang secara multi irisan dalam orde beberapa detik. Citra medik digital yang dihasilkan merepresentasikan penampang melintang dari berbagai struktur jaringan dari irisan yang dicitrakan. Salah satu tantangan yang dapat membantu dalam proses diagnosis berbasis citra adalah ekstraksi informasi dari struktur anatomi tertentu dengan suatu metode segmentasi citra serta visualisasi volumetrik dengan bantuan komputer. Untuk kasus visualisasi volumetrik tulang pelvis pada citra CT-scan multi irisan, seluruh citra yang mengandung bagian struktur tulang pelvis harus disegmentasi. Pada penelitian ini, satu teknik segmentasi citra berbasis active contour akan diimplementasikan untuk melakukan segmentasi citra multi irisan secara semi otomatis. Proses segmentasi citra diawalidengan menentukan model kurva 2D yang dilakukan secara manual pada citra irisan pertama. Kemudian model kurva tersebut secara iterasi akan berdeformasi sampai dengan bentuk kurva yang berhimpit pada batas tepian citra tulang pelvis. Hari akhir kurva 2D pada irisan pertama akan digunakan sebagai inisialisasi model kurva 2D pada proses segmentasi citra irisan berikutnya. Proses tersebut akan berlanjut sampai dengan citra irisan terakhir. Metode segmentasi citra berbasis active contour akan dibandingkan dengan metode segmentasi secara nilai ambang dari homogenitas distribusi intensitas dan metode segmentasi secara manual. Analisis secara kualitatif terhadap hasil segmentasi tiap irisan dan analisis kualitatif pada representasi visualisasi volumetrik digunakan pada penelitian ini.

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AbstractThe current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetricvisualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures must be segmented. In this research, an image segmentation technique based on active contour is implemented for semi-automatic multi slice image segmentation. Image segmentation steps are initialized with a define model of 2D curve on the first slice image manually. Next, its model curve is deformed to reach the final result of 2D curve that fits to boundary edges of pelvis bone image. The final result of 2D curve on previous slice image was used as an initialization model of 2D curve on the next slice images. This process will continue until the final slice image. This segmentation method was compared with the segmentation method based on threshold from homogenous intensitydistribution and manual segmentation method. Quantitative analysis from the results of segmentation on each slice and qualitative analysis on the representation of volumetric visualization are performed in this research."

"Teknologi visualisasi aliran sampai saat ini telah berkembang sangat pesat, visualisasi aliran berguna untuk memaparkan medan aliran ( medan vektor kecepatan, garis arus, vortisitas ) sesaat yang nantinya dapat diteliti lebih lanjut untuk dianalisa karakteristiknya. Salah satu alat visualisasi aliran dan software penunjangnya adalah PIV dan software imaging visualization. Output dari PIV diolah dengan menggunakan sofware tersebut. Image yang dihasilkan oleh PIV masih berupa data mentah yang belum bersifat informatif. data ini masih berupa gambaran aliran dari waktu ke waktu yang hanya menunjukkan lokasi dari sebaran tracer partice. Oleh karena itu image perlu ditinjau secara teliti dan diperlukan teknik pengolahan image agar menghasilkan parameter image yang baik. Teknik pengolahan image agar menghasilkan parameter image yang baik meliputi kalibrasi image, image masking, pengkontrasan image, cross correlation, masking cross correlation, filtering cross correlation, validation cross correlation, streamline, vortisitas dan plot vektor yang semuanya terangkum dalam paket PIV 2D.

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Flow visualization technology to date has been growing very rapidly, are useful for flow visualization flow field (velocity vector field, flow lines, vortices) shortly which will be investigated further to analyze its characteristics. One of the tools and software flow visualization PIV supporting is software imaging visualisation, the output of the PIV processed using the software. Image generated by the PIV still a raw data that has not been informative. The data is still a picture of the flow from time to time which only shows the location of the tracer particle distribution. Therefore image needs to be reviewed carefully and image processing techniques are required to produce image parameter is good. Image processing techniques in order to produce a good image parameters include image calibration, image masking, image contras, cross correlation, masking cross correlation, cross correlation filtering, validation cross correlation, streamline, vorticity and vector plots of which are summarized in 2D PIV package"