An intelligent decision support system for leukaemia diagnosis using microscopic blood images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

Photography and christian mission: George Brown`s images of the New Britain mission 1875-80

In 1875, Methodist George Brown arrived in the Bismarck Archipelago to establish the New Britain Mission. Based in the Duke of York Islands, Brown's territory covered New Ireland and the Gazelle Peninsula of New Britain. The mission was one of the first to be photographed from its inception. The Australian Museum holds 96 plates from the first five years of the mission. Brown's photographs are a visual record of conditions and peoples of the time. Analysed in relation to Brown's writings they are indicative of the relationships and bonds established through photography both in the mission field and across wider scientific and church audiences. The methodology employed here also challenges the kinds of interpretations of photographs that can arise from visual analyses relying solely on the caption and the posing of the subject.

Integrating local one-class classifiers for image retrieval

In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.

Indonesian masculinities : images of men in Indonesian TV advertising

This article examines images of men in Indonesian popular culture, focussing on TV advertising in particular.  Extending previous feminist scholarship on gendered representations in the Indonesian media, this article suggests that on the surface, Indonesian TV commercials reinforce and streobrthen male dominance in Indonesian society, promoting the notion that a man's primary roles are public ones, and a woman's primary roles are domestic ones. However, this investigation also suggests that so-called 'domesticated' women are also represented as actors in the public sphere, in roles as earners and spenders. This observation correlates with the argument that from the late New Order period, in media representation - and in some small sections of society, even in practice - women and men in Indonesia were becoming increasingly more equal (Sen, 1994, 2002). But is the pendulum of gender relations now swinging the other way? By examining Indonesian popular culture from the perspective of what it reveals about men and masculinity, this paper argues that a significant number of TV commercials may point towards another emergent trend in contemporary Indonesian popular culture: the rise of misandry, which is defined as a negative or contemptuous attitude towards men, 'the sexist counterpart of misogyny' (Nathanson and Young 2001, ix).

Proportional k-interval discretization for naive-Bayes classifiers

This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus seeks an appropriate trade-off between the bias and variance of the probability estimation for naive-Bayes classifiers. We justify PKID in theory, as well as test it on a wide cross-section of datasets. Our experimental results suggest that in comparison to its alternatives, PKID provides naive-Bayes classifiers competitive classification performance for smaller datasets and better classification performance for larger datasets.

A new approach for reducing blocking artefacts using linear filter for block based transform coded images

This paper presents a new approach to filtering the reconstructed image of block based transform coded images.

Reducing blocking artefacts in MPEG video coded images using entropic methods

In this paper investigation is made of the. use of entropic methods to identify smooth image blocks in order to filter the reconstructed image of block based transform coded images. We know that blocking artefact is Visually more prominent in the smooth region of a block rather than in the textured region. The entropic methods are employed to distinguish between smooth and textured blocks in a reconstructed image. The linear filtering is then carried out on the smooth region of the image to reduce the blocking artefact. The en tropic criteria investigated in this paper are: Shannon's (Logarithmic) Entropy, Exponential Entropy, and Quadratic Entropy.

Grid-based method for ranking images with multiple objects

One of the content-based image retrieval techniques is the shape-based technique, which allows users to ask for objects similar in shape to a query object. Sajjanhar and Lu proposed a method for shape representation and similarity measure called the grid-based method [1]. They have shown that the method is effective for the retrieval of segmented objects based on shape. In this paper, we describe a system which uses the grid-based method for retrieval of images with multiple objects. We perform experiments on the prototype system to compare the performance of the grid-based method with the Fourier descriptors method [2]. Preliminary results have been presented.

The rough sets feature selection for trees recognition in color aerial images using genetic algorithms

Selecting a set of features which is optimal for a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the relative reduct decision table of the rough set. This approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and finds the effective feature subset for texture classification . On the basis of the effective feature subset selected, this paper presents a method to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The experiments results show that the feature subset selected and the method of the object extraction presented in this paper are practical and effective.