Texture-based crowd detection and localisation

This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation

Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Preditcting Treatment Outcome Using Interpretable Models for Patients with Head and Neck Cancer

Head and neck cancer accounts for around 3 % of cancers worldwide, resulting in many deaths each year. The increasing number of patients receiving a cancer diagnosis increases the demand for accurate diagnosis and effective treatment. Intra-tumor heterogeneity is said to be one of the issues in cancer therapy, an issue that needs to be solved. Radiomics pave the way for extracting features based on the shape, size, and texture of the entire tumor. Radiomics extracts features from tumors based on the gray levels in a medical image. The process of radiomics is intended to capture texture and heterogeneity in the tumor that would be impossible to deduce from a simple tumor biopsy. Feature extraction by radiomics has been proven to enrich clinical datasets with valuable features that positively impact the performance of predictive models. This thesis investigates the use of clinical and radiomics features for predicting treatment outcomes of head and neck cancer patients using interpretable models. The radiomics algorithm extracts first-order statistical, shape, and texture features from PET and CT images of each patient. The 139 patients in the training dataset were from Oslo University Hospital (OUS), whereas the 99 patients in the test set were from the MAASTRO clinic in the Netherlands. All the clinical features, together with the radiomics features, counted 388 features in total. Feature selection through the repeated elastic net technique (RENT) was performed to exclude irrelevant features from the dataset. Seven different tree-based machine learning algorithms were fitted to the data, and the performance was validated by the accuracy, ROC AUC, Matthews correlation coefficient, F1 score for class 1, and F1 score for class 0. The models were tested on the external MAASTRO dataset, and the overall best-performing models were interpreted. On the external dataset from the MAASTRO clinic, the highest-performing models obtained an MCC of 0.37 for OS prediction and 0.44 for DFS prediction. For both OS and DFS, the highest predictions were made on only the clinical data. Transparency in machine learning models greatly benefits decision-makers in clinical settings, as every prediction can be reasoned for. Predicting treatment outcomes for head and neck patients is highly possible with interpretable models. To determine if the methods used in this thesis are suited for predicting treatment outcomes for head and neck cancer patients, it is necessary to test the methods and models on more datasets

Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy