Name
Abstract | ||
|---|---|---|
Medical imaging is an important medical diagnostic tool that provides visual information of the interior of the human body. In recent years computer-aided detection/diagnosis (CAD) systems became a key component of routine clinical practice in several medical areas, such as mammography and colonoscopy. However, research on brain CAD systems is still limited compared to other areas. This paper presents a solution that uses image processing techniques on brain computed tomography (CT) scans to create a CAD system that detects fresh bleeds. The system also features a basic classification method that distinguishes between an intra-axial and an extra-axial haemorrhage with the only limitation being subarachnoid haemorrhage (SAH), which is not always properly classified due to its complex structure. The techniques implemented include noise reduction methods, morphological operations and segmentation algorithms. The developed CAD system was tested on 36 brain CT sets obtained from the general hospital in Malta. Results show that the system achieves a sensitivity of 94.4%, a specificity of 94.4%, a precision of 91.259% and a classification accuracy of 88.89%. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EUROCON.2019.8861833 | IEEE EUROCON 2019 -18th International Conference on Smart Technologies |
Keywords | Field | DocType |
Computer-aided diagnosis,computed tomography,image processing,medical imaging | CAD,Noise reduction,Mammography,Segmentation,Computer science,Medical imaging,Image processing,Image segmentation,Radiology,Cad system | Conference |
ISBN | Citations | PageRank |
978-1-5386-9302-5 | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Napier | 1 | 0 | 0.34 |
| Carl James Debono | 2 | 38 | 11.66 |
| Paul Bezzina | 3 | 0 | 0.34 |
| Francis Zarb | 4 | 0 | 0.34 |