Abstract
This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a Multi-Level Logistic Markov-Gibs prior. This density favors neighbouring labels of the same class. The adopted discriminative classifier is the Fast Sparse Multinomial Regression. The discrete optimization problem one is led to is solved efficiently via graph cut tools. The effectiveness of the proposed method is evaluated, with simulated and real AVIRIS images, in two directions: 1) to improve the classification performance and 2) to decrease the size of the training sets.

Abstract
Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The effective implementation of such a tool could lead to a reduction in the number of cases selected for exeresis, with obvious benefits both to the patients and to the health care system. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) image segmentation, (ii) feature extraction and feature selection, (iii) lesion classification. This paper presents a review of the dermoscopic image analysis systems currently available, and an evaluation of the performance of one such systems, the Tuebinger Mole Analyser, with 83 dermoscopic images of melanocitic nevus.

Abstract
This paper presents an automatic system to extract and classify DNA profiles in a digital image. The system uses image segmentation and cumulative histogram analysis to identify both the number of layers in the image, and the number of profiles present in each layer. Once the individual profiles are located, their signatures are extracted and used to perform a hierarchical classification of the individual DNA profiles.

Abstract
Digital images are increasingly being used as steganographic covers for secret communication. The Least Significant Bit (LSB) encoding is one of the most widely used methods for embedding a message in a digital image. However, the direct application of LSB encoding is vulnerable to steganalysis. For example, RS steganalysis is very efficient in detecting the presence of a message in a digital image and to estimate its approximate size. This paper presents a method robust to RS steganalysis, that makes the presence of a message unnoticeable. The method is based on the application of reversible histogram transformation functions to the image, before and after embedding the secret message. The method was tested on 4 greyscale images, with messages of 10%, 30% and 90% of the maximum embedding size. The proposed method proved to be effective in eluding RS steganalysis for all cases tested.

Abstract
This paper describes an image processing system developed for automatic counting the number of collembola individuals on petri disks images. The system uses image segmentation and mathematical morphology techniques to identify and count the number of collembolans. The main challenges are the specular reflections at the edges of the circular samples and the foam present in a number of samples. The specular reflections are efficiently identified and removed by performing a two-stage segmentation. The foam is considered to be noise, as it is at cases difficult to discriminate between the foam and the collembola individuals. Morphological image processing tools are used both for noise reduction and for the identification of the collembolans. A total of 38 samples (divided in 3 groups according to their noise level) were tested and the results produced from the automatic system compared to the values available from manual counting. The relative error was on average 5.0% (3.4% for good quality samples, 4.6% for medium quality and 7.5% for poor quality samples).

Abstract
Image classification is often used to extract information from multi-spectral satellite images. Unsupervised methods can produce results well adjusted to the data, but that are usually difficult to assess. The purpose of this work was to evaluate the Xu internal similarity index ability to estimate the natural number of classes in multi-spectral satellite images. The performance of the index was initially tested with data produced synthetically. Four Landsat TM image sections were then used to evaluate the index. The test images were classified into a large number of classes, using the unsupervised algorithm ISODATA, which were subsequently structured hierarchically. The Xu index was used to identify the optimum partition for each test image. The results were analysed in the context of the land cover types expected for each location.