Image texture analysis of transvaginal ultrasound in monitoring ovarian cancer

Ovarian cancer has the highest mortality rate of all gynaecologic cancers and is the fifth most common cancer in UK women. It has been dubbed “the silent killer” because of its non-specific symptoms. Amongst various imaging modalities, ultrasound is considered the main modality for ovarian cancer triage. Like other imaging modalities, the main issue is that the interpretation of the images is subjective and observer dependent. In order to overcome this problem, texture analysis was considered for this study. Advances in medical imaging, computer technology and image processing have collectively ramped up the interest of many researchers in texture analysis. While there have been a number of successful uses of texture analysis technique reported, to my knowledge, until recently it has yet to be applied to characterise an ovarian lesion from a B-mode image. The concept of applying texture analysis in the medical field would not replace the conventional method of interpreting images but is simply intended to aid clinicians in making their diagnoses. Five categories of textural features were considered in this study: grey-level co-occurrence matrix (GLCM), Run Length Matrix (RLM), gradient, auto-regressive (AR) and wavelet. Prior to the image classification, the robustness or how well a specific textural feature can tolerate variation arises from the image acquisition and texture extraction process was first evaluated. This includes random variation caused by the ultrasound system and the operator during image acquisition. Other factors include the influence of region of interest (ROI) size, ROI depth, scanner gain setting, and „calliper line‟. Evaluation of scanning reliability was carried out using a tissue-equivalent phantom as well as evaluations of a clinical environment. iii Additionally, the reliability of the ROI delineation procedure for clinical images was also evaluated. An image enhancement technique and semi-automatic segmentation tool were employed in order to improve the ROI delineation procedure. The results of the study indicated that two out of five textural features, GLCM and wavelet, were robust. Hence, these two features were then used for image classification purposes. To extract textural features from the clinical images, two ROI delineation approaches were introduced: (i) the textural features were extracted from the whole area of the tissue of interest, and (ii) the anechoic area within the normal and malignant tissues was excluded from features extraction. The results revealed that the second approach outperformed the first approach: there is a significant difference in the GLCM and wavelet features between the three groups: normal tissue, cysts, and malignant. Receiver operating characteristic (ROC) curve analysis was carried out to determine the discriminatory ability of textural features, which was found to be satisfactory. The principal conclusion was that GLCM and wavelet features can potentially be used as computer aided diagnosis (CAD) tools to help clinicians in the diagnosis of ovarian cancer

Focusing the Field of a HIFU Array Transducer through Human Ribs

High intensity focused ultrasound (HIFU) enables highly localised, non-invasive tissue ablation, and its efficacy in the treatment of a range of cancers, including those of the kidney, prostate and breast has been demonstrated. HIFU offers the ability to treat deep-seated tumours locally, and potentially bears fewer side effects than more established treatment modalities such as resection, chemotherapy and ionising radiation. There remain, however, a number of significant challenges which currently hinder its widespread clinical application. One of these challenges is the need to transmit sufficient energy through the ribcage to ablate tissue at the required foci whilst minimising the formation of side lobes and sparing healthy tissue. Ribs both absorb and reflect ultrasound strongly. This sometimes results in overheating of bone and overlying tissue during treatment, leading to skin burns. Successful treatment of a patient with tumours in the upper abdomen therefore requires a thorough understanding of the way acoustic and thermal energy is deposited. In this thesis, an approach which predicts the acoustic field of a multi-element HIFU array scattered by human ribs, the topology of which was obtained from CT scan data, has been developed, implemented and validated. It is based on the boundary element method (BEM). Dissipative mechanisms were introduced into the propagating medium, along with a complex surface impedance condition at the surface of the ribs. A reformulation of the boundary element equations as a constrained optimisation problem was carried out to solve the inverse problem of determining the complex surface normal velocities of a multi-element HIFU array that best fitted a required acoustic pressure distribution in a least-squares sense. This was done whilst ensuring that an acoustic dose rate parameter at the surface of the ribs was kept below a specified threshold. The methodology was tested at an excitation frequency of 1 MHz on a spherical section multi-element array in the presence of human ribs. It was compared on six array-rib topologies against other methods of focusing through the ribs, including binarised apodisation based on geometric ray tracing, phase conjugation and the DORT method (décomposition de l’opérateur de retournement temporel). The constrained optimisation approach offers greater potential than the other focusing methods in terms of maximising the ratio of acoustic pressure magnitudes at the focus to those on the surface of the ribs whilst taking full advantage of the dynamic range of the phased array

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

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis