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"It is realized that an important thing in medical image visualization serving is to be able to see human as observe. Nevertheless, certain noise is rising in image acquisition causes image quality is reducing. An image involvement is a process in which an image can be best analyzed. Denoising is a one of the image enhancement techniques. An adaptive thresholding technique based wavelet serves to reduce noise from medical image. A discrete wavalet transformation is used in this research. The STH (Soft Thresholding), HTH (Hard Thresholding), and MPTH (Multiscale Products Thresholding) methods are used to calculate and compare as medical image Denoising results. Two criteria, MSR (Mean-to-Standard Deviation Ratio) and CNR (Contrast-to-Noise Ratio) have proposed to perform as Denoising at medical image. From the result, it can be concluded that denoising by using MPTH (Multiscale Products Thresholding) method, the values of MSR (Mean-to-Standard Deviation Ratio), CNR (Contrast-to-Noise Ratio) are greater than STH (Soft Thresholding), and HTH (Hard Thresholding) can be obtained."

"This volume comprises the latest developments in both fundamental science and patient-specific applications, discussing topics such as: cellular mechanics, injury biomechanics, biomechanics of the heart and vascular system, algorithms of computational biomechanics for medical image analysis, and both patient-specific fluid dynamics and solid mechanics simulations. With contributions from researchers world-wide, Computational Biomechanics for Medicine: Measurments, Models, and Predictions provides an opportunity for specialists in the field to present their latest methodologies and advancements."

"White Matter Hyperintensities (WMH) adalah area di otak yang memiliki intensitas yang lebih tinggi dibandingkan dengan area normal lainnya pada hasil pemindaian Magnetic Resonance Imaging (MRI). WMH seringkali terkait dengan penyakit pembuluh kecil di otak, sehingga deteksi dini WMH sangat penting. Namun, terdapat dua masalah umum dalam mendeteksi WMH, yaitu ambiguitas yang tinggi dan kesulitan dalam mendeteksi WMH yang berukuran kecil. Dalam penelitian ini, kami mengusulkan metode yang disebut Probabilistic TransUNet untuk mengatasi masalah segmentasi objek WMH yang berukuran kecil dan ambiguitas yang tinggi pada citra medis. Kami melakukan eksperimen K-fold cross validation untuk mengukur kinerja model. Berdasarkan hasil eksperimen, model berbasis Transformer (TransUNet dan Probabilistic TransUNet) lebih baik dan presisi dalam melakukan segmentasi pada obyek WMH yang berukuran kecil, hal ini ditunjukkan oleh nilai Dice Similarity Coefficient (DSC) yang dihasilkan lebih tinggi dibandingkan dengan model berbasis Convolutional Nueral Networks (CNN) (U-Net dan Probabilistic U-Net). Penambahan probabilistic model dan pendekatan berbasis transformer berhasil mendapatkan performa yang lebih baik. Metode yang kami usulkan berhasil mendapatkan nilai DSC sebesar 0,744 dalam 5-fold cross validation, lebih baik dari metode sebelumnya. Dalam melakukan segmentasi objek kecil metode usulan kami mendapatkan nilai DSC sebesar 0,51.......White Matter Hyperintensities (WMH) are areas of the brain that have a higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of WMH important. However, there are two common issues in detecting WMH: high ambiguity and difficulty detecting small WMH. In this study, we propose a method called Probabilistic TransUNet to address the precision of small object segmentation and the high ambiguity of medical images. We conducted a k-fold cross-validation experiment to measure model performance. Based on the experiments, Transformer-based models (TransUNet and Probabilistic TransUNet) were found to provide more precise and better segmentation results, as demonstrated by the higher DSC scores obtained compared to CNN-based models (U-Net and Probabilistic U-Net) and their ability to segment small WMH objects. The proposed method obtained a DSC score of 0742 in k-fold cross-validation, better than the previous method. In conducting segmentation of small objects, our proposed method achieved a DSC score of 0,51."

"Kompresi citra medis telah lama menjadi suatu penerapan teknologi kompresi yang kontroversial. Terdapat dua jenis kompresi citra: lossless dan lossy. Nilai rasio kompresi pada lossless compression tidak terlalu besar sehinga lossy compression diterapkan. Perhatian utama pada kompresi citra medis adalah penentuan visually lossless threshold dimana ditentukan nilai batas kuantisasi sehingga kompresi terhadap citra medis dapat dilakukan sebelum terjadi distorsi yang tertangkap oleh sistem visual manusia. Pada skripsi ini dilakukan simulasi kompresi citra medis berdasarkan contrast threshold dan visual masking yang diujikan pada modality computed tomography, mammography, dan X-ray. Evaluasi subjektif dilakukan untuk menilai kualitas citra medis hasil dekompresi dengan melibatkan dokter spesialis radiologi sebagai penguji. Tingkat kejelasan algoritma yang digunakan pada citra medis pada modality X-ray, mammography, dan computed tomography masing-masing adalah 90,00%, 85,00%, dan 89,1666667%. Rasio kompresi dan waktu yang diperlukan untuk melakukan kompresi dan dekompresi bervariasi pada setiap citra medis yang diujikan.

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Medical image compression has been a controversial application of compression technology. There are two types of image compression: lossless and lossy. The value of compression ratio in the lossless compression is not too big so that lossy compression is applied. The main concern in the medical image compression is the determination of visually lossless threshold limit of quantitation where values are determined so that the compression of medical images can be applied before the distortion is captured by the human visual system. In this thesis, a simulation of medical image compression based on contrast threshold and visual masking is tested on modalities: computed tomography, mammography, and X-ray. Subjective evaluation was conducted to assess the quality of medical image decompression results involving radiologist as testers. The degree of clarity of the algorithms used in medical image on the modality of X-rays, mammography, and computed tomography, respectively are 90,00%, 85,00%, and 89,1666667%. Compression ratio and time required to perform compression and decompression vary on each medical image tested."

"This book introduces advanced and hybrid compression techniques specifically used for medical images. The book discusses conventional compression and compressive sensing (CS) theory based approaches that are designed and implemented using various image transforms, such as: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) and greedy based recovery algorithm. The authors show how these techniques provide simulation results of various compression techniques for different types of medical images, such as MRI, CT, US, and x-ray images. Future research directions are provided for medical imaging science. The book will be a welcomed reference for engineers, clinicians, and research students working with medical image compression in the biomedical imaging field. Covers various algorithms for data compression and medical image compression;Provides simulation results of compression algorithms for different types of medical images;Provides study of compressive sensing theory for compression of medical images."

"This book highlights the latest research presented at the International Conference on Translational Medicine and Imaging (ICTMI) 2017. This event brought together the worlds leading scientists, engineers and clinicians from a wide range of disciplines in the field of medical imaging. Bioimaging has continued to evolve across a wide spectrum of applications from diagnostics and personalized therapy to the mechanistic understanding of biological processes, and as a result there is ever-increasing demand for more robust methods and their integration with clinical and molecular data. This book presents a number of these methods."