Active Contour Model for Ultrasound Images with Rayleigh Distribution

Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method

The quest for "diagnostically lossless" medical image compression using objective image quality measures

Given the explosive growth of digital image data being generated, medical communities worldwide have recognized the need for increasingly efficient methods of storage, display and transmission of medical images. For this reason lossy image compression is inevitable. Furthermore, it is absolutely essential to be able to determine the degree to which a medical image can be compressed before its “diagnostic quality” is compromised. This work aims to achieve “diagnostically lossless compression”, i.e., compression with no loss in visual quality nor diagnostic accuracy. Recent research by Koff et al. has shown that at higher compression levels lossy JPEG is more effective than JPEG2000 in some cases of brain and abdominal CT images. We have investigated the effects of the sharp skull edges in CT neuro images on JPEG and JPEG 2000 lossy compression. We provide an explanation why JPEG performs better than JPEG2000 for certain types of CT images. Another aspect of this study is primarily concerned with improved methods of assessing the diagnostic quality of compressed medical images. In this study, we have compared the performances of structural similarity (SSIM) index, mean squared error (MSE), compression ratio and JPEG quality factor, based on the data collected in a subjective experiment involving radiologists. An receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov analyses indicate that compression ratio is not always a good indicator of visual quality. Moreover, SSIM demonstrates the best performance. We have also shown that a weighted Youden index can provide SSIM and MSE thresholds for acceptable compression. We have also proposed two approaches of modifying L2-based approximations so that they conform to Weber’s model of perception. We show that the imposition of a condition of perceptual invariance in greyscale space according to Weber’s model leads to the unique (unnormalized) measure with density function ρ(t) = 1/t. This result implies that the logarithmic L1 distance is the most natural “Weberized” image metric. We provide numerical implementations of the intensity-weighted approximation methods for natural and medical images

Retina-Inspired and Physically Based Image Enhancement

Images and videos with good lightness and contrast are vital in several applications, where human experts make an important decision based on the imaging information, such as medical, security, and remote sensing applications. The well-known image enhancement methods include spatial and frequency enhancement techniques such as linear transformation, gamma correction, contrast stretching, histogram equalization and homomorphic filtering. Those conventional techniques are easy to implement but do not recover the exact colour of the images; hence they have limited application areas. Conventional image/video enhancement methods have been widely used with their different advantages and drawbacks; since the last century, there has been increased interest in retina-inspired techniques, e.g., Retinex and Cellular Neural Networks (CNN) as they attempt to mimic the human retina. Despite considerable advances in computer vision techniques, the human eye and visual cortex by far supersede the performance of state-of-the-art algorithms. This research aims to propose a retinal network computational model for image enhancement that mimics retinal layers, targeting the interconnectivity between the Bipolar receptive field and the Ganglion receptive field. The research started by enhancing two state-of-the-art image enhancement methods through their integration with image formation models. In particular, physics-based features (e.g. Spectral Power Distribution of the dominant illuminate in the scene and the Surface Spectral Reflectance of the objects contained in the image are estimated and used as inputs for the enhanced methods). The results show that the proposed technique can adapt to scene variations such as a change in illumination, scene structure, camera position and shadowing. It gives superior performance over the original model. The research has successfully proposed a novel Ganglion Receptive Field (GRF) computational model for image enhancement. Instead of considering only the interactions between each pixel and its surroundings within a single colour layer, the proposed framework introduces the interaction between different colour layers to mimic the retinal neural process; to better mimic the centre-surround retinal receptive field concept, different photoreceptors' outputs are combined. Additionally, this thesis proposed a new contrast enhancement method based on Weber's Law. The objective evaluation shows the superiority of the proposed Ganglion Receptive Field (GRF) method over state-of-the-art methods. The contrast restored image generated by the GRF method achieved the highest performance in contrast enhancement and luminance restoration; however, it achieved less performance in structure preservation, which confirms the physiological studies that observe the same behaviour from the human visual system

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system