Feature detection in ultrasound images for computer aided diagnosis of Hodgkin's Lymphoma

Abstract

The varying clinical presentation of Hodgkin's lymphoma (HL) poses a diagnostic challenge in South Africa, as the clinical picture of this lymphoma overlaps with prevalent comorbidities such as tuberculosis (TB) and the Human Immuno-Deficiency Virus (HIV). HIV infection additionally increases the risk of developing HL. These factors motivate for the need to investigate the role of imaging modalities in the diagnostic pathway of HL. The goal of this project was to develop and evaluate an automated framework for improving diagnostic imaging interpretability of ultrasound for HL diagnosis in a HIV TB endemic environment. To achieve this, a precise abdominal ultrasound protocol was developed with clinical guidance. The specific frames in the protocol were used to detect several image biomarkers of clinical interest: splenic enlargement (splenomegaly), splenic lesions, splenic microabscesses, abdominal lymph node enlargement, ascites, and effusions (pleural and pericardial). The developed protocol provided a novel guideline to identify an abnormality from the available ultrasound images. A secondary outcome of the protocol was the development of a prospective guide to image Hodgkin's lymphoma patients using ultrasound, however further testing and evaluation is required to validate its use. Image processing techniques were then applied to identified frames, and geometrical and textural features extracted, to develop an automated abnormality characterisation framework. A total of 36 features were extracted and used to characterise each abnormality. Thereafter, an automated algorithm was used to characterise and classify Hodgkin's lymphoma. A support vector machine model was built, with two experiments performed to evaluate the model. The model achieved a maximum training accuracy of 83%, similar in performance to support vector machine classification models used in medical applications. Noticeably the classification accuracy increased favourably when specific abnormalities were assessed: an enlarged spleen, splenic micro abscesses, ascites, pleural effusions, and pericardial effusions. This may indicate that these specific abnormalities are sufficient to differentiate patients with and without Hodgkin's lymphoma but understanding the reasoning for the decision taken by the system requires further investigation. In this study we show how image processing and automated classification techniques when applied to ultrasound images, have the potential to improve the differential diagnostic pathway of HL. Further evaluation using a larger dataset is planned, to validate and implement these findings in a strained healthcare setting.