An efficient hybrid approach for medical images enhancement

Medical images have various critical usages in the field of medical science and healthcare engineering. These images contain information about many severe diseases. Health professionals identify various diseases by observing the medical images. Quality of medical images directly affects the accuracy of detection and diagnosis of various diseases. Therefore, quality of images must be as good as possible. Different approaches are existing today for enhancement of medical images, but quality of images is not good. In this literature, we have proposed a novel approach that uses principal component analysis (PCA), multi-scale switching morphological operator (MSMO) and contrast limited adaptive histogram equalization (CLAHE) methods in a unique sequence for this purpose. We have conducted exhaustive experiments on large number of images of various modalities such as MRI, ultrasound, and retina. Obtained results demonstrate that quality of medical images processed by proposed approach has significantly improved and better than other existing methods of this field

Contrast enhancement using grey scale transformation techniques

The object of this thesis has been to examine grey scale transformation techniques in order to incorporate them into a system for automatically selecting a technique to enhance the contrast in a given image. In order to include existing techniques in the system it was necessary to examine each in detail, and to understand under what conditions it gave good results. It was found that a number of techniques had only a limited scope or suffered from some problem in its design. This led to the development of a new technique based on the display capabilities of a monitor; the adaptation of another technique, globed histogram equalisation, to make it applicable to a wider range of images and the modification of the local histogram equalisation algorithm to smooth different sized regions of the image to the same degree. The resultant algorithms, together with those existing in the literature, were included in the system. The system provides an interactive environment for selecting grey scale transformation techniques. The usual method of choosing a contrast enhancement technique is to apply it, look at the result, discard it if the result is not suitable, or if there is a parameter value to be set, modify its value, and try the technique again. Here a more systematic approach is tried using ideas from Knowledge Based Systems and Object Oriented Systems. A model of the way contrast enhancement techniques are selected is encoded into the system and is used with information obtained by analysing the image (either automatic analysis done by the system, or interactive analysis done with the aid of the user) to select the most appropriate techniques. The techniques selected by the system have to fulfil three quite demanding criteria, ensuring that the system is a reliable and useful tool

Interactive Evolutionary Algorithms for Image Enhancement and Creation

Image enhancement and creation, particularly for aesthetic purposes, are tasks for which the use of interactive evolutionary algorithms would seem to be well suited. Previous work has concentrated on the development of various aspects of the interactive evolutionary algorithms and their application to various image enhancement and creation problems. Robust evaluation of algorithmic design options in interactive evolutionary algorithms and the comparison of interactive evolutionary algorithms to alternative approaches to achieving the same goals is generally less well addressed. The work presented in this thesis is primarily concerned with different interactive evolutionary algorithms, search spaces, and operators for setting the input values required by image processing and image creation tasks. A secondary concern is determining when the use of the interactive evolutionary algorithm approach to image enhancement problems is warranted and how it compares with alternative approaches. Various interactive evolutionary algorithms were implemented and compared in a number of specifically devised experiments using tasks of varying complexity. A novel aspect of this thesis, with regards to other work in the study of interactive evolutionary algorithms, was that statistical analysis of the data gathered from the experiments was performed. This analysis demonstrated, contrary to popular assumption, that the choice of algorithm parameters, operators, search spaces, and even the underlying evolutionary algorithm has little effect on the quality of the resulting images or the time it takes to develop them. It was found that the interaction methods chosen when implementing the user interface of the interactive evolutionary algorithms had a greater influence on the performances of the algorithms

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

Curvilinear Structure Enhancement in Biomedical Images

Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing. Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis. In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts. First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images. Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions. Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D

Machine learning approach to thermite weld defects detection and classification.

Masters Degree. University of KwaZulu- Natal, Durban.The defects formed during the thermite welding process between two sections of rails require the welded joints to be inspected for quality purpose. The commonly used non-destructive method for inspection is Radiography testing. However, the detection and classification of various defects from the generated radiography imagesremains a costly, lengthy and subjective process as it is purely conducted manually by trained experts. It has been shown that most rail breaks occur due to a crack that initiated from the weld joint defect that was not detected. To meet the requirements of the modern technologies, the development of an automated detection and classification model is significantly demanded by the railway industry. This work presents a method based on image processing and machine learning techniques to automatically detect and classify welding defects. Radiography images are first enhanced using the Contrast Limited Adaptive Histogram Equalisation method; thereafter, the Chan-Vese Active Contour Model is applied to the enhanced images to segment and extract the weld joint as the Region of Interest from the image background. A comparative investigation between the Local Binary Patterns descriptor and the Bag of Visual Words approach with Speeded Up Robust Features descriptor was carried out for extracting features in the weld joint images. The effectiveness of the aforementioned feature extractors was evaluated using the Support Vector Machines, K-Nearest Neighbours and Naive Bayes classifiers. This study’s experimental results showed that the Bag of Visual Words approach when used with the Support Vector Machines classifier, achieves the best overall classification accuracy of 94.66%. The proposed method can be expanded in other industries where Radiography testing is used as the inspection tool

Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram Ekualisasi

Penggunaan kamera Closed Circuit Television (CCTV) banyak digunakan saat ini terutama perusahaan atau industry, pertokoan dan tempat-tempat strategis lainnya. Dengan menggunakan kamera CCTV keamanan tempat tersebut akan terjamin. Akan tetapi kekurangan dari kamera CCTV adalah apabila ruangan tidak memiliki cahaya kurang (gelap) maka hasil objek yang terekam tidak maksimal. Untuk mengatasi permasalahan diatas maka perlu adanya Histogram Ekualisasi yang dapat memberikan perbaikan kualitas citra (Image Enhancement). Untuk citra yang diambil sebagai Image Enhancement sebagai contoh uji coba penelitian adalah citra Screen Capture CCTV. Dari citra Screen Capture CCTV tersebut dilakukan tahap-tahap pemrosesan citra untuk menghasilkan perbaikan kualitas citra yang baik. Rencana pengambilan data citra screen Capture CCTV sejumlah 5 citra. Cara kerja sistem informasi Image Enhancement adalah citra screen Capture CCTV di praproses dengan ukuran 200x260 piksel BMP, kemudian citra di GarayScale untuk dijadikan nilai piksel yang seragam, selanjutnya citra di histogram ekualisasi untuk intensitas menjadi seragam. Untuk menguji tingkat kualitas citra menggunakan metode ekstraksi tekstur histogram berbasis rata-rata intensitas dan deviasi standar. 

Prominent region of interest contrast enhancement for knee MR images: data from the OAI

Osteoarthritis is the most commonly seen arthritis, where there are 30.8 million adults affected in 2015. Magnetic resonance imaging (MRI) plays a key role to provide direct visualization and quantitative measurement on knee cartilage to monitor the osteoarthritis progression. However, the visual quality of MRI data can be influenced by poor background luminance, complex human knee anatomy, and indistinctive tissue contrast. Typical histogram equalisation methods are proven to be irrelevant in processing the biomedical images due to their steep cumulative density function (CDF) mapping curve which could result in severe washout and distortion on subject details. In this paper, the prominent region of interest contrast enhancement method (PROICE) is proposed to separate the original histogram of a 16-bit biomedical image into two Gaussians that cover dark pixels region and bright pixels region respectively. After obtaining the mean of the brighter region, where our ROI – knee cartilage falls, the mean becomes a break point to process two Bezier transform curves separately. The Bezier curves are then combined to replace the typical CDF curve to equalize the original histogram. The enhanced image preserves knee feature as well as region of interest (ROI) mean brightness. The image enhancement performance tests show that PROICE has achieved the highest peak signal-to-noise ratio (PSNR=24.747±1.315dB), lowest absolute mean brightness error (AMBE=0.020±0.007) and notably structural similarity index (SSIM=0.935±0.019). In other words, PROICE has considerably outperformed the other approaches in terms of its noise reduction, perceived image quality, its precision and has shown great potential to visually assist physicians in their diagnosis and decision-making process

Põhjalik uuring ülisuure dünaamilise ulatusega piltide toonivastendamisest koos subjektiivsete testidega

A high dynamic range (HDR) image has a very wide range of luminance levels that traditional low dynamic range (LDR) displays cannot visualize. For this reason, HDR images are usually transformed to 8-bit representations, so that the alpha channel for each pixel is used as an exponent value, sometimes referred to as exponential notation [43]. Tone mapping operators (TMOs) are used to transform high dynamic range to low dynamic range domain by compressing pixels so that traditional LDR display can visualize them. The purpose of this thesis is to identify and analyse differences and similarities between the wide range of tone mapping operators that are available in the literature. Each TMO has been analyzed using subjective studies considering different conditions, which include environment, luminance, and colour. Also, several inverse tone mapping operators, HDR mappings with exposure fusion, histogram adjustment, and retinex have been analysed in this study. 19 different TMOs have been examined using a variety of HDR images. Mean opinion score (MOS) is calculated on those selected TMOs by asking the opinion of 25 independent people considering candidates’ age, vision, and colour blindness

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