Alex F. de Araujo

New artificial life model for image enhancement

Abstract In this work, a method to enhance images based on a new artificial life model is presented. The model is inspired on the behavior of a herbivore organism, when this organism is in a certain environment and selects its food. This organism travels through the image iteratively, selecting the more suitable food and eating parts of it in each iteration. The path that the organism travels through in the image is defined by a priori knowledge about the environment and how it should move in it. Here, we modeled the control and perception centers of the organism, as well as the simulation of its actions and effects on the environment. To demonstrate the efficiency of our method quantitative and qualitative results of the enhancement of synthetic and real images with low contrast and different levels of noise are presented. Obtained results confirm the ability of the new artificial life model for improving the contrast of the objects in the input images.

Identification of foliar diseases in cotton crop

The manifestation of pathogens in plantations is the most important cause of losses in several crops. These usually represent less income to the farmers due to the lower product quality as well as higher prices to the consumer due to the smaller offering of goods. The sooner the disease is identified the sooner one can control it through the use of agrochemicals, avoiding great damages to the plantation. This chapter introduces a method for the automatic classification of cotton diseases based on the feature extraction of foliar symptoms from digital images. The method uses the energy of the wavelet transform for feature extraction and a Support Vector Machine for the actual classification. Five possible diagnostics are provided: (1) healthy (SA), (2) injured with Ramularia disease (RA), (3) infected with Bacterial Blight (BA), (4) infected with Ascochyta Blight (AS), or (5) possibly infected with an unknown disease.

Smoothing of ultrasound images using a new selective average filter

A new method is proposed to perform selective smoothing of images affected by speckle noise.A new smoothing criterion is defined for the average smoothing filter.The convolution window of the smoothing filter is adjustable.The method is evaluated using real ultrasound medical images based on image quality metrics.The proposed method produced better results than the current methods evaluated. Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.

Efficient parallelization on GPU of an image smoothing method based on a variational model

Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments.