Image Automatic Categorisation using Selected Features Attained from Integrated Non-Subsampled Contourlet with Multiphase Level Sets

A framework of automatic detection and categorization of Breast Cancer (BC) biopsy images utilizing significant interpretable features is initially considered in discussed work. Appropriate efficient techniques are engaged in layout steps of the discussed framework. Different steps include 1.To emphasize the edge particulars of tissue structure; the distinguished Non-Subsampled Contourlet (NSC) transform is implemented. 2. For the demarcation of cells from background, k-means, Adaptive Size Marker Controlled Watershed, two proposed integrated methodologies were discussed. Proposed Method-II, an integrated approach of NSC and Multiphase Level Sets is preferred to other segmentation practices as it proves better performance 3. In feature extraction phase, extracted 13 shape morphology, 33 textural (includes 6 histogram, 22 Haralick’s, 3 Tamura’s, 2 Graylevel Run-Length Matrix,) and 2 intensity features from partitioned tissue images for 96 trained image

Medical Image Segmentation: Thresholding and Minimum Spanning Trees

I bildesegmentering deles et bilde i separate objekter eller regioner. Det er et essensielt skritt i bildebehandling for å definere interesseområder for videre behandling eller analyse. Oppdelingsprosessen reduserer kompleksiteten til et bilde for å forenkle analysen av attributtene oppnådd etter segmentering. Det forandrer representasjonen av informasjonen i det opprinnelige bildet og presenterer pikslene på en måte som er mer meningsfull og lettere å forstå. Bildesegmentering har forskjellige anvendelser. For medisinske bilder tar segmenteringsprosessen sikte på å trekke ut bildedatasettet for å identifisere områder av anatomien som er relevante for en bestemt studie eller diagnose av pasienten. For eksempel kan man lokalisere berørte eller anormale deler av kroppen. Segmentering av oppfølgingsdata og baseline lesjonssegmentering er også svært viktig for å vurdere behandlingsresponsen. Det er forskjellige metoder som blir brukt for bildesegmentering. De kan klassifiseres basert på hvordan de er formulert og hvordan segmenteringsprosessen utføres. Metodene inkluderer de som er baserte på terskelverdier, graf-baserte, kant-baserte, klynge-baserte, modell-baserte og hybride metoder, og metoder basert på maskinlæring og dyp læring. Andre metoder er baserte på å utvide, splitte og legge sammen regioner, å finne diskontinuiteter i randen, vannskille segmentering, aktive kontuter og graf-baserte metoder. I denne avhandlingen har vi utviklet metoder for å segmentere forskjellige typer medisinske bilder. Vi testet metodene på datasett for hvite blodceller (WBCs) og magnetiske resonansbilder (MRI). De utviklede metodene og analysen som er utført på bildedatasettet er presentert i tre artikler. I artikkel A (Paper A) foreslo vi en metode for segmentering av nukleuser og cytoplasma fra hvite blodceller. Metodene estimerer terskelen for segmentering av nukleuser automatisk basert på lokale minima. Metoden segmenterer WBC-ene før segmentering av cytoplasma avhengig av kompleksiteten til objektene i bildet. For bilder der WBC-ene er godt skilt fra røde blodlegemer (RBC), er WBC-ene segmentert ved å ta gjennomsnittet av $n$ bilder som allerede var filtrert med en terskelverdi. For bilder der RBC-er overlapper WBC-ene, er hele WBC-ene segmentert ved hjelp av enkle lineære iterative klynger (SLIC) og vannskillemetoder. Cytoplasmaet oppnås ved å trekke den segmenterte nukleusen fra den segmenterte WBC-en. Metoden testes på to forskjellige offentlig tilgjengelige datasett, og resultatene sammenlignes med toppmoderne metoder. I artikkel B (Paper B) foreslo vi en metode for segmentering av hjernesvulster basert på minste dekkende tre-konsepter (minimum spanning tree, MST). Metoden utfører interaktiv segmentering basert på MST. I denne artikkelen er bildet lastet inn i et interaktivt vindu for segmentering av svulsten. Fokusregion og bakgrunn skilles ved å klikke for å dele MST i to trær. Ett av disse trærne representerer fokusregionen og det andre representerer bakgrunnen. Den foreslåtte metoden ble testet ved å segmentere to forskjellige 2D-hjerne T1 vektede magnetisk resonans bildedatasett. Metoden er enkel å implementere og resultatene indikerer at den er nøyaktig og effektiv. I artikkel C (Paper C) foreslår vi en metode som behandler et 3D MRI-volum og deler det i hjernen, ikke-hjernevev og bakgrunnsegmenter. Det er en grafbasert metode som bruker MST til å skille 3D MRI inn i de tre regiontypene. Grafen lages av et forhåndsbehandlet 3D MRI-volum etterfulgt av konstrueringen av MST-en. Segmenteringsprosessen gir tre merkede, sammenkoblende komponenter som omformes tilbake til 3D MRI-form. Etikettene brukes til å segmentere hjernen, ikke-hjernevev og bakgrunn. Metoden ble testet på tre forskjellige offentlig tilgjengelige datasett og resultatene ble sammenlignet med ulike toppmoderne metoder.In image segmentation, an image is divided into separate objects or regions. It is an essential step in image processing to define areas of interest for further processing or analysis. The segmentation process reduces the complexity of an image to simplify the analysis of the attributes obtained after segmentation. It changes the representation of the information in the original image and presents the pixels in a way that is more meaningful and easier to understand. Image segmentation has various applications. For medical images, the segmentation process aims to extract the image data set to identify areas of the anatomy relevant to a particular study or diagnosis of the patient. For example, one can locate affected or abnormal parts of the body. Segmentation of follow-up data and baseline lesion segmentation is also very important to assess the treatment response. There are different methods used for image segmentation. They can be classified based on how they are formulated and how the segmentation process is performed. The methods include those based on threshold values, edge-based, cluster-based, model-based and hybrid methods, and methods based on machine learning and deep learning. Other methods are based on growing, splitting and merging regions, finding discontinuities in the edge, watershed segmentation, active contours and graph-based methods. In this thesis, we have developed methods for segmenting different types of medical images. We tested the methods on datasets for white blood cells (WBCs) and magnetic resonance images (MRI). The developed methods and the analysis performed on the image data set are presented in three articles. In Paper A we proposed a method for segmenting nuclei and cytoplasm from white blood cells. The method estimates the threshold for segmentation of nuclei automatically based on local minima. The method segments the WBCs before segmenting the cytoplasm depending on the complexity of the objects in the image. For images where the WBCs are well separated from red blood cells (RBCs), the WBCs are segmented by taking the average of $n$ images that were already filtered with a threshold value. For images where RBCs overlap the WBCs, the entire WBCs are segmented using simple linear iterative clustering (SLIC) and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC. The method is tested on two different publicly available datasets, and the results are compared with state of the art methods. In Paper B, we proposed a method for segmenting brain tumors based on minimum spanning tree (MST) concepts. The method performs interactive segmentation based on the MST. In this paper, the image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the MST into two trees. One of these trees represents the region of interest and the other represents the background. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The method is simple to implement and the results indicate that it is accurate and efficient. In Paper C, we propose a method that processes a 3D MRI volume and partitions it into brain, non-brain tissues, and background segments. It is a graph-based method that uses MST to separate the 3D MRI into the brain, non-brain, and background regions. The graph is made from a preprocessed 3D MRI volume followed by constructing the MST. The segmentation process produces three labeled connected components which are reshaped back to the shape of the 3D MRI. The labels are used to segment the brain, non-brain tissues, and the background. The method was tested on three different publicly available data sets and the results were compared to different state of the art methods.Doktorgradsavhandlin

Quantitative Analysis of Ultrasound Images of the Preterm Brain

In this PhD new algorithms are proposed to better understand and diagnose white matter damage in the preterm Brain. Since Ultrasound imaging is the most suited modality for the inspection of brain pathologies in very low birth weight infants we propose multiple techniques to assist in what is called Computer-Aided Diagnosis. As a main result we are able to increase the qualitative diagnosis from a 70% detectability to a 98% quantitative detectability

Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring

INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.Funded by National Radiotherapy Trials Quality Assurance (RTTQA) grou

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms

WNT-DEPENDENT REGENERATIVE FUNCTION IS INDUCED IN LEUKEMIA-INITIATING AC133BRIGHT CELLS

The Cancer Stem Cell model supported the notion that leukemia was initiated and maintained in vivo by a small fraction of leukemia-initiating cells (LICs). Previous studies have suggested the involvement of Wnt signaling pathway in Acute Myeloid Leukemia (AML) by the ability to sustain the development of LICs. A novel hematopoietic stem and progenitor cell marker, monoclonal antibody AC133, recognizes the CD34bright CD38- subset of human acute myeloid leukemia cells, suggesting that it may be an early marker for the LICs. During the first part of my phD program we previously evaluated the ability of leukemic AC133+ fraction, to perform engraftment following to xenotransplantation in immunodeficient mouse model Rag2-/-\u3b3c-/-. The results showed that the surface marker AC133 is able to enrich for the cell fraction that contains the LICs. In consideration of our previously reported data, derived from the expression profiling analysis performed in normal (n=10) and leukemic (n=33) human long-term reconstituting AC133+ cells, we revealed that the ligand-dependent Wnt signaling is induced in AML through a diffuse expression and release of WNT10B, a hematopoietic stem cells regenerative-associated molecule. In situ detection performed on bone marrow biopsies of AML patients, showed the activation of the Wnt pathway, through the concomitant presence of the ligand WNT10B and of the active dephosphorylated \u3b2-catenin form, suggesting an autocrine / paracrine-type ligand-dependent activation mechanism. In consideration of the link between hematopoietic regeneration and developmental signaling, we transplanted primary AC133+ AML A46 cells into developing zebrafish. This biosensor model revealed the formation of ectopic structures by activation of dorsal organizer markers that act downstream of the Wnt pathway. These results suggested that the misappropriating Wnt associated functions can promote pathological stem cell-like regeneration responsiveness. The analyses performed in situ retained information on the cellular localization, enabling determination of the activity status of individual cells and allowing the tumor environment view. Taking this issue into consideration, during the second part of my phD program, I set up the application of a new in situ method for localized detection and genotyping of individual transcripts directly in cells and tissues. The mRNA in situ detection technique is based on padlock probes ligation and target priming rolling circle amplification allowing the single nucleotide resolution in heterogenous tissues. The mRNA in situ detection performed on bone marrow biopsies derived from AML patients, showed a diffuse localization pattern of WNT10B molecule in the tissue. Conversely, only the AC133bright cell population shows the Wnt signaling activation signature represented by the cytoplasmatic accumulation and nuclear translocation of the active form of \u3b2-catenin. In spite of this, we previously evidenced that the regenerative function of WNT signaling pathway is defined by the up-regulation of WNT10B, WNT10A, WNT2B and WNT6 loci, we identified the WNT10B as a major locus associated with the regenerative function and over-expressed by all AML patients. By the molecular evaluation of the WNT10B transcript, we isolated an aberrant splicing variant (WNT10BIVS1), that identify Non Core-Binding Factor Leukemia (NCBFL) class and whose potential role is discussed. Moreover, we demonstrate that the function of "leukemia stem cell", present in the cell population enriched for the marker AC133bright, is strictly related to regenerative function associated with WNT signaling, defining the key role of WNT10B ligand as a specific molecular marker for leuchemogenesis. This thesis defines the new suitable approaches to characterize the leukemia-initiating cells (LICs) and suggest the role of WNT10B as a new suitable target for AML

Computer analysis for registration and change detection of retinal images

The current system of retinal screening is manual; It requires repetitive examination of a large number of retinal images by professional optometrists who try to identify the presence of abnormalities. As a result of the manual and repetitive nature of such examination, there is a possibility for error in diagnosis, in particular in the case when the progression of disease is slight. As the sight is an extremely important sense, any tools which can improve the probability of detecting disease could be considered beneficial. Moreover, the early detection of ophthalmic anomalies can prevent the impairment or loss of vision. The study reported in this Thesis investigates computer vision and image processing techniques to analyse retinal images automatically, in particular for diabetic retinopathy disease which causes blindness. This analysis aims to automate registration to detect differences between a pair of images taken at different times. These differences could be the result of disease progression or, occasionally, simply the presence of artefacts. The resulting methods from this study, will be therefore used to build a software tool to aid the diagnosis process undertaken by ophthalmologists. The research also presents a number of algorithms for the enhancement and visualisation of information present within the retinal images, which under normal situations would be invisible to the viewer; For instance, in the case of slight disease progression or in the case of similar levels of contrast between images, making it difficult for the human eye to see or to distinguish any variations. This study also presents a number of developed methods for computer analysis of retinal images. These methods include a colour distance measurement algorithm, detection of bifurcations and their cross points in retina, image registration, and change detection. The overall analysis in this study can be classified to four stages: image enhancement, landmarks detection, registration, and change detection. The study has showed that the methods developed can achieve automatic, efficient, accurate, and robust implementation