As computer technologies are developed rapidly in recent years, the ways to diagnose diseases also alter in clinical practice. Picture Archiving and Communication System (PACS) is an example that makes the diagnostic way for medical images change from view box to monitor. All types of medical images tend to be digitized and this makes it practical for helping doctor diagnose medical images via computer technologies. In this thesis, we propose a systemic approach to screen abnormalities in chest radiographs. First, a preprocess step identifying the view of chest radiographs is introduced. Second, a method is proposed for automated detection of gross abnormal opacity in chest radiographs. Third, computation time reduction is performed by subsampling. Finally, a computer-aided diagnosis system is implemented and evaluated in a clinical environment. Major technique used in this thesis is to analyze the projection profile obtained by projecting a chest image on to the mediolateral axis. The discriminant performance for each method is evaluated by using receiver operating characteristic (ROC) analysis. The results indicate that the proposed methods are potentially useful for screening abnormalities in chest radiographs.

Background. Mass impregnated high voltage cables are used in, for example, submarine electric power transmission. One of the production steps of such cables is the lapping process in which several hundred layers of special purpose paper are wrapped around the conductor of the cable. It is important for the mechanical and electrical properties of the finished cable that the paper is applied correctly, however there currently exists no reliable way of continuously ensuring that the paper is applied correctly. Objective. The objective of this thesis is to develop a prototype of a cost-effective machine vision system which monitors the lapping process and detects and records any errors that may occur during the process; with an accuracy of at least one tenth of a millimetre. Methods. The requirements of the system are specified and suitable hardware is identified. Using a method where the images are projected down to one axis as well as other signal processing methods, the errors are measured. Experiments are performed where the accuracy and performance of the system is tested in a controlled environment. Results. The results show that the system is able to detect and measure errors accurately down to one tenth of a millimetre while operating at a frame rate of 40 frames per second. The hardware cost of the system is less than €200. Conclusions. A cost-effective machine vision system capable of performing measurements accurate down to one tenth of a millimetre can be implemented using the inexpensive Raspberry Pi 3 and Raspberry Pi Camera Module V2. Th