an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information

position. That is, knowledge of spatial receiver sensitivity implies information about the origin of detected MR signals, which may be utilized for image generation. Unlike position in k-space, sensitivity is a receiver property and does not refer to the state of the object under examination. Therefore

Quantitative analysis of MR images is becoming increasingly important in clinical trials in multiple sclerosis (MS). This paper describes a fully automated atlas-based technique for segmenting MS lesions from large data sets of multi-channel MR images. The method simultaneously estimates

Analysis Society) mammogram database have been used and the results allowed us to draw a FROC (Free Response Receiver Operating Characteristic) curve. When compared the FROC curve with similar systems experts, the high ability of our system was confirmed. In this system, images of different thresholds

We present a general method to study the dissymmetry of anatomical structures such as the human brain. Our method relies on the estimate of 3D dissymmetry fields, the use of 3D vector field operators, and statistics to compute significance maps. We also present a fully automated implementation

for analysis, interpretation, and understanding of images. Accuracy of the extracted features decides the accuracy of the algorithm. Selection of a suitable algorithm is highly based on the application. This paper highlights the various image segmentation algorithms, used in medical images.

In this paper, the segmentation of magnetic resonance brain (MR) images has been analysed using K-Means clustering, Fuzzy C-Means clustering (FCM) and Pulse Coupled Neural Network (PCNN) techniques. All the three methods are applied to normal and abnormal brain MR images and the results

In this study, we would like to present brain tumor detection methods, based on the conventional K-means technique, Expectation Maximization (EM) algorithm and a new Spatial Fuzzy-technique analysis of brain MR images. Though, the K-means and EM algorithm were already used in Brain MR image

in the study of the human brain. Computer aided diagnosis systems for detecting Brain tumor for medical purpose have been investigated using several techniques. In this Review paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI

is present or absent. A fuzzy clustering-based technique is proposed which helps to study & analyze the intricate structure of the brain, hence can be used as a visual analysis and a study tool.