SHAPE FROM FOCUS USING LULU OPERATORS AND DISCRETE PULSE TRANSFORM IN THE PRESENCE OF NOISE

A study of three dimension (3D) shape recovery is an interesting and challenging area of research. Recovering the depth information of an object from normal two dimensional (2D) images has been studied for a long time with different techniques. One technique for 3D shape recovery is known as Shape from Focus (SFF). SFF is a method that depends on different focused values in reconstructing the shape, surface, and depth of an object. The different focus values are captured by taking different images for the same object by varying the focus length or varying the distance between object and camera. This single view imaging makes the data gathering simpler in SFF compared to other shape recovery techniques. Calculating the shape of the object using different images with different focused values can be done by applying sharpness detection methods to maximize and detect the focused values. However, noise destroys many information in an image and the result of noise corruption can change the focus values in the images. This thesis presents a new 3D shape recovery technique based on focus values in the presence of noise. The proposed technique is based on LULU operators and Discrete Pulse Transform (DPT). LULU operators are nonlinear rank selector operators that hold consistent separation, total variation and shape preservation properties. The proposed techniques show better and more accurate performance in comparison with the existing SFF techniques in noisy environment

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

Monitoring of lung cancer patients during radiotherapy using combined texture and level set analysis of CBCT images

In the UK, radiotherapy (RT) contributes to a large amount of lung cancer treatment while its imaging information is limited to computed tomography (CT) and cone beam CT (CBCT) images. The oncologists defines the gross tumour volume (GTV) manually on the planning-CT images before any treatment starts. Manual contouring suffers from many disadvantages and the bad quality of CBCT images makes it very challenging for the clinicians to observe tumour behaviour in the time of treatment. CBCT is the only kind of image available throughout the whole course of RT which is used in the mechanical procedure of adjusting patient position before starting each session of treatment and is not generally used by clinicians for monitoring the tumour. The goal of this thesis is to develop a tumour detection model of non-small cell lung tumours on CBCT images in the course of treatment. By developing this process clinicians will be greatly aided in their role, helping them to detect lung tumours to allow better diagnosis and improving patient treatment outcome. Therefore a new segmentation approach is proposed as combined texture analysis and level set model. It has the potential capability to track the variation of the tumour shape over time of treatment solely using CBCT images, and evaluate the accountability of RT for different patients. The texture analysis, second-order statistics obtained from gray level co-occurrence matrices (GLCM), highlight the tumour boundary and help Chan-Vese and Li level set models convergence in the segmentation process. Further on a new parallel level sets model is proposed by combining Chan-Vese and Li models in the concept of vector-valued image level set. This new approach overcomes the difficulties in the parameter settings of current models by giving more freedom of choice in tuning parameters as well as selecting level set models. All proposed models were evaluated on the dataset of fifty different patients suffered from non-small cell lung cancer. For the validation procedure, qualitative analysis was carried out by an oncologist as there is no ground truth in each CBCT image during RT. The decision of the oncologist based on patient history has proven the results of this work. For quantitative analysis, the Dice coefficient is used to evaluate the tumour segmentation results on CBCT compared to GTV on CT images prior to treatment to evaluate the amount of changes especially after one third of RT on CBCT #10. Additionally, the proposed segmentation models had the accuracy of almost 90% to the GTV delineated by the oncologist for the only one patient in the dataset having GTV on CBCT images which proved the ability of these models for further analysis during the absence of GTV on CBCTs. For improving this research and helping the clinicians at most, the proposed segmentation model can be used as a notification model to assist clinicians for a better understanding of the tumour during RT and subsequent use in offline adaptive radiotherapy (ART)

SHAPE FROM FOCUS USING LULU OPERATORS AND DISCRETE PULSE TRANSFORM IN THE PRESENCE OF NOISE

A study of three dimension (3D) shape recovery is an interesting and challenging area of research. Recovering the depth information of an object from normal two dimensional (2D) images has been studied for a long time with different techniques. One technique for 3D shape recovery is known as Shape from Focus (SFF). SFF is a method that depends on different focused values in reconstructing the shape, surface, and depth of an object. The different focus values are captured by taking different images for the same object by varying the focus length or varying the distance between object and camera. This single view imaging makes the data gathering simpler in SFF compared to other shape recovery techniques. Calculating the shape of the object using different images with different focused values can be done by applying sharpness detection methods to maximize and detect the focused values. However, noise destroys many information in an image and the result of noise corruption can change the focus values in the images. This thesis presents a new 3D shape recovery technique based on focus values in the presence of noise. The proposed technique is based on LULU operators and Discrete Pulse Transform (DPT). LULU operators are nonlinear rank selector operators that hold consistent separation, total variation and shape preservation properties. The proposed techniques show better and more accurate performance in comparison with the existing SFF techniques in noisy environment

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: I) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace, the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature. The input device used for this signature verification system is 5DT Data Glove 14 Ultra which is originally design for virtual reality application. The output of the data glove, which captures the dynamic process in the signing action, is the data matrix, A to be processed for feature extraction and matching. This work is divided into two parts. In part I, we investigate the performance of the SVD-based signature verification system using a new matching technique, that is, by calculating the average distance between the different subspaces. In part IJ, we investigate the performance of the signature verification with reducedsensor data glove. To select the 7-most prominent sensors of the data glove, we calculate the F-value for each sensor and choose 7 sensors that gives the highest Fvalue

Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival

Funder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Abstract: Background: Measurement of volumetric features is challenging in glioblastoma. We investigate whether volumetric features derived from preoperative MRI using a convolutional neural network–assisted segmentation is correlated with survival. Methods: Preoperative MRI of 120 patients were scored using Visually Accessible Rembrandt Images (VASARI) features. We trained and tested a multilayer, multi-scale convolutional neural network on multimodal brain tumour segmentation challenge (BRATS) data, prior to testing on our dataset. The automated labels were manually edited to generate ground truth segmentations. Network performance for our data and BRATS data was compared. Multivariable Cox regression analysis corrected for multiple testing using the false discovery rate was performed to correlate clinical and imaging variables with overall survival. Results: Median Dice coefficients in our sample were (1) whole tumour 0.94 (IQR, 0.82–0.98) compared to 0.91 (IQR, 0.83–0.94 p = 0.012), (2) FLAIR region 0.84 (IQR, 0.63–0.95) compared to 0.81 (IQR, 0.69–0.8 p = 0.170), (3) contrast-enhancing region 0.91 (IQR, 0.74–0.98) compared to 0.83 (IQR, 0.78–0.89 p = 0.003) and (4) necrosis region were 0.82 (IQR, 0.47–0.97) compared to 0.67 (IQR, 0.42–0.81 p = 0.005). Contrast-enhancing region/tumour core ratio (HR 4.73 [95% CI, 1.67–13.40], corrected p = 0.017) and necrotic core/tumour core ratio (HR 8.13 [95% CI, 2.06–32.12], corrected p = 0.011) were independently associated with overall survival. Conclusion: Semi-automated segmentation of glioblastoma using a convolutional neural network trained on independent data is robust when applied to routine clinical data. The segmented volumes have prognostic significance

Semi-automated construction of patient individualised clinical target volumes for radiotherapy treatment of glioblastoma utilising diffusion tensor decomposition maps.

OBJECTIVES: Glioblastoma multiforme (GBM) is a highly infiltrative primary brain tumour with an aggressive clinical course. Diffusion tensor imaging (DT-MRI or DTI) is a recently developed technique capable of visualising subclinical tumour spread into adjacent brain tissue. Tensor decomposition through p and q maps can be used for planning of treatment. Our objective was to develop a tool to automate the segmentation of DTI decomposed p and q maps in GBM patients in order to inform construction of radiotherapy target volumes. METHODS: Chan-Vese level set model is applied to segment the p map using the q map as its initial starting point. The reason of choosing this model is because of the robustness of this model on either conventional MRI or only DTI. The method was applied on a data set consisting of 50 patients having their gross tumour volume delineated on their q map and Chan-Vese level set model uses these superimposed masks to incorporate the infiltrative edges. RESULTS: The expansion of tumour boundary from q map to p map is clearly visible in all cases and the Dice coefficient (DC) showed a mean similarity of 74% across all 50 patients between the manually segmented ground truth p map and the level set automatic segmentation. CONCLUSION: Automated segmentation of the tumour infiltration boundary using DTI and tensor decomposition is possible using Chan-Vese level set methods to expand q map to p map. We have provided initial validation of this technique against manual contours performed by experienced clinicians. ADVANCES IN KNOWLEDGE: This novel automated technique to generate p maps has the potential to individualise radiation treatment volumes and act as a decision support tool for the treating oncologist.This study was funded by an NIHR Clinician Scientist Fellowship for a SJP, project reference NIHR/CS/009/011. The research was supported by the NIHR Brain Injury MedTech Co-operative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge and the NIHR Cambridge BRC. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Also, RR is supported as part of the CRUK-funded PRaM-GBM study (C9216/A19732). NVIDIA Corporation is gratefully acknowledged for the donation of two Titan X GPUs for our research

Multi-scale segmentation in GBM treatment using diffusion tensor imaging.

Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p - shown to represent tumor invasion) and the anisotropic component (q - shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.CRUK Project grant - PRaM-GBM study (C9216/A19732) NIHR Clinician Scientist Fellowship (project reference NIHR/CS/009/011) and an NIHR Career Development Fellowship (project reference CDF-2018-11-ST2-003) for SJP

Online Signature Verification using SVD Method

Online signature verification rests on hypothesis which any writer has similarity among signature samples, with scale variability and small distortion. This is a dynamic method in which users sign and then biometric system recognizes the signature by analyzing its characters such as acceleration, pressure, and orientation. The proposed technique for online signature verification is based on the Singular Value Decomposition (SVD) technique which involves four aspects: 1) data acquisition and preprocessing 2) feature extraction 3) matching (classification), 4) decision making. The SVD is used to find r-singular vectors sensing the maximal energy of the signature data matrix A, called principle subspace thus account for most of the variation in the original data. Having modeled the signature through its r-th principal subspace. the authenticity of the tried signature can be determined by calculating the average distance between its principal subspace and the template signature