Motivation: Segmentation is a fundamental problem in medical image understanding. Most of the further analysis relies on the results of the segmentation procedure. Unfortunately, standard image processing techniques fail to deliver satisfying results for most medical applications. Many types of tissues have similar magnetic characteristics, and therefore have overlapping intensity ranges in the...

This article surveys deformable models, a promising and vigorously researched computerassisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (...

background: we aimed to extract the histogram features for text analysis and, to classify the types of bio medical waste (bmw) for garbage disposal and management. methods: the given bmw was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. after that, the histogram features of the filtered image were extracted with the help of proposed modifi...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...

This paper illustrates an algorithm for osteosarcoma segmentation, using vectorial fuzzy-connectedness segmentation, and coming up with a methodology which can be used to segment some distinct tissues of osteosarcoma such as tumor, necrosis and parosteal sarcoma from 3D vectorial images. However, fuzzy-connectedness segmentation can be successfully used only in connected regions. In this paper,...

This paper starts with giving the medical imaging modalities that are in practical use and lists several of the new medical imaging modalities under development. The remainder of the paper is concentrated on progress in MR imaging, ultrasound imaging and x-ray CT imaging. These modalities are major radiological imaging tools, which will have growing significance in the next decade. They are sur...

Segmentation is one of the main tasks in medical image analysis. Measuring the quality of 3D segmentation algorithms is an essential requirement during development and for evaluation. Various methods exist to measure the quality of a segmentation with respect to a reference segmentation. Validating interactive 3D segmentation approaches or methods for 3D segmentation editing is more complex, ho...

BACKGROUND Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medica...

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results.

Due to the increasing demand for more medical images in clinical practices for better assessment and diagnosis, medical image analysis has gained more importance than ever. In this chapter, we will focus on the subarea of anatomical structure detection and segmentation, which plays an important role in speeding up the diagnostic work flow. Although remarkable progresses have made in detecting a...