Automatic ROI detection and classification of the Achilles tendon ultrasound images

Ultrasound (US) imaging plays an important role in medical imaging technologies. It is widely used because of its ease of use and low cost compared to other imaging techniques. Specifically, ultrasound imaging is used in the detection of the Achilles Tendon (AT) pathologies as it detects important details. For example, US imaging is used for AT rupture that affects about 1 in 5,000 people worldwide. Decision support systems are important in medical imaging, as they assist radiologist in detecting probable diagnoses and lesions. The work presented in this paper concerns the development of a software application to detect changes in the AT ultrasound images and subsequently classify them into normal or abnormal. We propose an approach that fully automates the detection for the Region of Interest (ROI) in ultrasound AT images. The original image is divided into six blocks with 1 cm size in each direction. The blocks lie inside the vulnerable area considered as our ROI. The proposed system achieved an accuracy of 97.21%

Mitotic cell detection in H&E stained meningioma histopathology slides

Indiana University-Purdue University Indianapolis (IUPUI)Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science

Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images

Noise is one of the major problems that hinder an effective texture analysis of disease in medical images, which may cause variability in the reported diagnosis. In this paper seven texture measurement methods (two wavelet, two model and three statistical based) were applied to investigate their susceptibility to subtle noise caused by acquisition and reconstruction deficiencies in computed tomography (CT) images. Features of lung tumours were extracted from two different conventional and contrast enhanced CT image data-sets under filtered and noisy conditions. When measuring the noise in the background open-air region of the analysed CT images, noise of Gaussian and Rayleigh distributions with varying mean and variance was encountered, and Fishers’ distance was used to differentiate between an original extracted lung tumour region of interest (ROI) with the filtered and noisy reconstructed versions. It was determined that the wavelet packet (WP) and fractal dimension measures were the least affected, while the Gaussian Markov random field, run-length and co-occurrence matrices were the most affected by noise. Depending on the selected ROI size, it was concluded that texture measures with fewer extracted features can decrease susceptibility to noise, with the WP and the Gabor filter having a stable performance in both filtered and noisy CT versions and for both data-sets. Knowing how robust each texture measure under noise presence is can assist physicians using an automated lung texture classification system in choosing the appropriate feature extraction algorithm for a more accurate diagnosis

Slantlet transform-based segmentation and α -shape theory-based 3D visualization and volume calculation methods for MRI brain tumour

Magnetic Resonance Imaging (MRI) being the foremost significant component of medical diagnosis which requires careful, efficient, precise and reliable image analyses for brain tumour detection, segmentation, visualisation and volume calculation. The inherently varying nature of tumour shapes, locations and image intensities make brain tumour detection greatly intricate. Certainly, having a perfect result of brain tumour detection and segmentation is advantageous. Despite several available methods, tumour detection and segmentation are far from being resolved. Meanwhile, the progress of 3D visualisation and volume calculation of brain tumour is very limited due to absence of ground truth. Thus, this study proposes four new methods, namely abnormal MRI slice detection, brain tumour segmentation based on Slantlet Transform (SLT), 3D visualization and volume calculation of brain tumour based on Alpha (α) shape theory. In addition, two new datasets along with ground truth are created to validate the shape and volume of the brain tumour. The methodology involves three main phases. In the first phase, it begins with the cerebral tissue extraction, followed by abnormal block detection and its fine-tuning mechanism, and ends with abnormal slice detection based on the detected abnormal blocks. The second phase involves brain tumour segmentation that covers three processes. The abnormal slice is first decomposed using the SLT, then its significant coefficients are selected using Donoho universal threshold. The resultant image is composed using inverse SLT to obtain the tumour region. Finally, in the third phase, four original ideas are proposed to visualise and calculate the volume of the tumour. The first idea involves the determination of an optimal α value using a new formula. The second idea is to merge all tumour points for all abnormal slices using the α value to form a set of tetrahedrons. The third idea is to select the most relevant tetrahedrons using the α value as the threshold. The fourth idea is to calculate the volume of the tumour based on the selected tetrahedrons. In order to evaluate the performance of the proposed methods, a series of experiments are conducted using three standard datasets which comprise of 4567 MRI slices of 35 patients. The methods are evaluated using standard practices and benchmarked against the best and up-to-date techniques. Based on the experiments, the proposed methods have produced very encouraging results with an accuracy rate of 96% for the abnormality slice detection along with sensitivity and specificity of 99% for brain tumour segmentation. A perfect result for the 3D visualisation and volume calculation of brain tumour is also attained. The admirable features of the results suggest that the proposed methods may constitute a basis for reliable MRI brain tumour diagnosis and treatments

Técnicas basadas en kernel para el análisis de texturas en imagen biomédica

[Resumen] En problemas del mundo real es relevante el estudio de la importancia de todas las variables obtenidas de manera que sea posible la eliminación de ruido, es en este punto donde surgen las técnicas de selección de variables. El objetivo de estas técnicas es pues encontrar el subconjunto de variables que describan de la mejor manera posible la información útil contenida en los datos permitiendo mejorar el rendimiento. En espacios de alta dimensionalidad son especialmente interesantes las técnicas basadas en kernel, donde han demostrado una alta eficiencia debido a su capacidad para generalizar en dichos espacios. En este trabajo se realiza una nueva propuesta para el análisis de texturas en imagen biomédica mediante la integración, utilizando técnicas basadas en kernel, de diferentes tipos de datos de textura para la selección de las variables más representativas con el objetivo de mejorar los resultados obtenidos en clasificación y en interpretabilidad de las variables obtenidas. Para validar esta propuesta se ha formalizado un diseño experimental con cuatro fases diferenciadas: extracción y preprocesado de los datos, aprendizaje y selección del mejor modelo asegurando la reproducibilidad de los resultados a la vez que una comparación en condiciones de igualdad.[Resumo] En problemas do mundo real é relevante o estudo da importancia de todas as variables obtidas de maneira que sexa posible a eliminación de ruído, é neste punto onde xorden as técnicas de selección de variables. O obxectivo destas técnicas é pois encontrar o subconxunto de variables que describan do mellor xeito posible a información útil contida nos datos permitindo mellorar o rendemento. En espazos de alta dimensionalidade son especialmente interesantes as técnicas baseadas en kernel, onde demostraron unha alta eficiencia debido á súa capacidade para xeneralizar nos devanditos espazos. Neste traballo realízase unha nova proposta para a análise de texturas en imaxe biomédica mediante a integración, utilizando técnicas baseadas en kernel, de diferentes tipos de datos de textura para a selección das variables máis representativas co obxectivo de mellorar os resultados obtidos en clasificación e en interpretabilidade das variables obtidas. Para validar esta proposta formalizouse un deseño experimental con catro fases diferenciadas: extracción e preprocesar dos datos, aprendizaxe e selección do mellor modelo asegurando a reproducibilidade dos resultados á vez que unha comparación en condicións de igualdade. Utilizáronse imaxes de xeles de electroforese bidimensional.[Abstract] In real-world problems it is of relevance to study the importance of all the variables obtained, so that denoising could be possible, because it is at this point when the variable selection techniques arise. Therefore, these techniques are aimed at finding the subset of variables that describe' in the best possible way the useful information contained in the data, allowing improved performance. In high-dimensional spaces, the kernel-based techniques are of special relevance, as they have demonstrated a high efficiency due to their ability to generalize in these spaces. In this work, a new approach for texture analysis in biomedical imaging is performed by means of integration. For this procedure, kernel-based techniques were used with different types of texture data for the selection of the most representative variables in order to improve the results obtained in classification and interpretability of the obtained variables. To validate this proposal, an experimental design has been concluded, consisting of four different phases: 1) Data extraction; 2) Data pre-processing; 3) Learning and 4) Selection of the best model to ensure the reproducibility of results while making a comparison under conditions of equality. In this regard, two-dimensional electrophoresis gel images have been used

An image processing decisional system for the Achilles tendon using ultrasound images

The Achilles Tendon (AT) is described as the largest and strongest tendon in the human body. As for any other organs in the human body, the AT is associated with some medical problems that include Achilles rupture and Achilles tendonitis. AT rupture affects about 1 in 5,000 people worldwide. Additionally, AT is seen in about 10 percent of the patients involved in sports activities. Today, ultrasound imaging plays a crucial role in medical imaging technologies. It is portable, non-invasive, free of radiation risks, relatively inexpensive and capable of taking real-time images. There is a lack of research that looks into the early detection and diagnosis of AT abnormalities from ultrasound images. This motivated the researcher to build a complete system which enables one to crop, denoise, enhance, extract the important features and classify AT ultrasound images. The proposed application focuses on developing an automated system platform. Generally, systems for analysing ultrasound images involve four stages, pre-processing, segmentation, feature extraction and classification. To produce the best results for classifying the AT, SRAD, CLAHE, GLCM, GLRLM, KPCA algorithms have been used. This was followed by the use of different standard and ensemble classifiers trained and tested using the dataset samples and reduced features to categorize the AT images into normal or abnormal. Various classifiers have been adopted in this research to improve the classification accuracy. To build an image decisional system, a 57 AT ultrasound images has been collected. These images were used in three different approaches where the Region of Interest (ROI) position and size are located differently. To avoid the imbalanced misleading metrics, different evaluation metrics have been adapted to compare different classifiers and evaluate the whole classification accuracy. The classification outcomes are evaluated using different metrics in order to estimate the decisional system performance. A high accuracy of 83% was achieved during the classification process. Most of the ensemble classifies worked better than the standard classifiers in all the three ROI approaches. The research aim was achieved and accomplished by building an image processing decisional system for the AT ultrasound images. This system can distinguish between normal and abnormal AT ultrasound images. In this decisional system, AT images were improved and enhanced to achieve a high accuracy of classification without any user intervention