A. Payne

Publications (4)2.83 Total impact

• Article: LV wall segmentation using the variational level set method (LSM) with additional shape constraint for oedema quantification.

  K Kadir, H Gao, A Payne, J Soraghan, C Berry
  [show abstract] [hide abstract]
  ABSTRACT: In this paper an automatic algorithm for the left ventricle (LV) wall segmentation and oedema quantification from T2-weighted cardiac magnetic resonance (CMR) images is presented. The extent of myocardial oedema delineates the ischaemic area-at-risk (AAR) after myocardial infarction (MI). Since AAR can be used to estimate the amount of salvageable myocardial post-MI, oedema imaging has potential clinical utility in the management of acute MI patients. This paper presents a new scheme based on the variational level set method (LSM) with additional shape constraint for the segmentation of T2-weighted CMR image. In our approach, shape information of the myocardial wall is utilized to introduce a shape feature of the myocardial wall into the variational level set formulation. The performance of the method is tested using real CMR images (12 patients) and the results of the automatic system are compared to manual segmentation. The mean perpendicular distances between the automatic and manual LV wall boundaries are in the range of 1-2 mm. Bland-Altman analysis on LV wall area indicates there is no consistent bias as a function of LV wall area, with a mean bias of -121 mm(2) between individual investigator one (IV1) and LSM, and -122 mm(2) between individual investigator two (IV2) and LSM when compared to two investigators. Furthermore, the oedema quantification demonstrates good correlation when compared to an expert with an average error of 9.3% for 69 slices of short axis CMR image from 12 patients.
  Physics in Medicine and Biology 09/2012; 57(19):6007-23. · 2.83 Impact Factor
• Conference Proceeding: Variational level set method with shape constraint and application to oedema cardiac magnetic resonance image

  K. Kadir, Hao Gao, A. Payne, J. Soraghan, C. Berry
  [show abstract] [hide abstract]
  ABSTRACT: Quantification of oedema area after acute myocardial infarction (MI) is very important in clinical prognosis for differentiating the viable and death myocardial tissues. In order to quantify oedema region, the first step is to segment the myocardial wall accurately. This paper applies variational level set method with shape constraint to oedema cardiac magnetic resonance (CMR) images. Shape information of the myocardial wall is introduced into the variational level set formulation, and the performance of the automatic method is tested on T2 weighted CMR images from 8 patients, and compared with manual analysis from two clinical experts. Results show that the proposed automatic segmentation framework can segment left ventricle (LV) boundary with no significant difference compared to manual segmentation.
  Digital Signal Processing (DSP), 2011 17th International Conference on; 08/2011
• Source

  Available from: mit.edu

  Conference Proceeding: Automatic quantification of oedema from T2 weighted CMR image using a Hybrid Thresholding Oedema Sizing Algorithm (HTOSA)

  K. Kadir, A. Payne, J.J. Soraghan, C. Berry
  [show abstract] [hide abstract]
  ABSTRACT: Oedema is fluid retention within the myocardial tissue due to damage tissue causing swelling in the affected area after myocardial infarction (MI). Quantification of oedema area after an MI is an important step in medical prognosis to differentiate between viable and death myocardial tissue. In this paper a novel technique of Hybrid Thresholding Oedema Sizing Algorithm (HTOSA) is presented. To quantify the oedema a hybrid technique based on combination of morphological operation combined with statistical thresholding is used. The performance of the method was tested on real T2 weighted MRI data. The quantitative result of the automatic method compare to manual segmentation by a skill clinician is very encouraging with correlation score of 81.1%.
  Computing in Cardiology, 2010; 10/2010
• Source

  Available from: Kushsairy Kadir

  Chapter: Automatic Left Ventricle Segmentation in T2 Weighted CMR Images

  Kurhairy Abdul Kadir, A. Payne, John J. Soraghan, C. Berry
  [show abstract] [hide abstract]
  ABSTRACT: An automatic left ventricle (LV) segmentation method for T2 weighted Cardiac Magnetic Resonance (CMR) image is presented. The method takes multi-slice T2 weighted CMR images from the basal to the apex of the heart. Inter-slice and intra-slice fuzzy reasoning is used to guide the centre point detection for each slice. Morphological filtering is used in the reconstruction to homogenise the blood pool region. Then radial search Fuzzy Multiscale Edge Detection (FMED) is used to segment the endocardium and the epicardium of the LV. Evaluation of the method is performed on 6 patient with approximately 42 slices of real T2 weighted MRI data. The quantitative result of the automatic method compared to those obtained from manual segmentation by a skilled clinician are very encouraging, with correlation scores of 96.2% correlation for the endocardium and 85.7% correlation for the epicardium.
  10/2010: pages 247-254;