One major hallmark of the Alzheimer's disease (AD) is the loss of neurons in the brain. In many cases, medical experts
use magnetic resonance imaging (MRI) to qualitatively measure the neuronal loss by the shrinkage or enlargement of the
structures-of-interest. Brain ventricle is one of the popular choices. It is easily detectable in clinical MR images due to the
high contrast of the cerebro-spinal fluid (CSF) with the rest of the parenchyma. Moreover, atrophy in any periventricular
structure will directly lead to ventricle enlargement. For quantitative analysis, volume is the common choice. However,
volume is a gross measure and it cannot capture the entire complexity of the anatomical shape. Since most existing shape
descriptors are complex and difficult-to-reproduce, more straightforward and robust ways to extract ventricle shape features
are preferred in the diagnosis. In this paper, a novel ventricle shape based classification method for Alzheimer's disease has
been proposed. Training process is carried out to generate two probability maps for two training classes: healthy controls
(HC) and AD patients. By subtracting the HC probability map from the AD probability map, we get a 3D ventricle
discriminant map. Then a matching coefficient has been calculated between each training subject and the discriminant
map. An adjustable cut-off point of the matching coefficients has been drawn for the two classes. Generally, the higher
the cut-off point that has been drawn, the higher specificity can be achieved. However, it will result in relatively lower
sensitivity and vice versa. The benchmarked results against volume based classification show that the area under the ROC
curves for our proposed method is as high as 0.86 compared with only 0.71 for volume based classification method.
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our
method is motivated by the observation that neighboring or coupling objects in images generate configurations
and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs
coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate
kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape
distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on
such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm
based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted
objects in a number of applications. In particular for medical image analysis, we use our method to
extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging
segmentation problem. We also apply our technique to the problem of handwritten character segmentation.
Finally, we use our method to segment cars in urban scenes.
Diagnosis accuracy in the medical field, is mainly affected by either lack of sufficient understanding of some diseases or the inter/intra-observer variability of the diagnoses. We believe that mining of large medical databases can help improve the current
status of disease understanding and decision making. In a previous study based on binary description of hypointensity in the brain, it was shown that brain iron accumulation shape provides additional information to the shape-insensitive features, such as the total brain iron load, that are commonly used in clinics. This paper
proposes a novel, nonbinary description of hypointensity in the brain based on principal component analysis. We compare the complementary and redundant information provided by the two
descriptions using Kendall's rank correlation coefficient in order to better understand the individual descriptions of iron accumulation in the brain and obtain a more robust and accurate search and retrieval system.