Takis Kasparis

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the experts assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis.

Integrated System for the Complete Segmentation of the Common Carotid Artery Bifurcation in Ultrasound Images

The complete segmentation of the common carotid artery (CCA) bifurcation in ultrasound images is important for the evaluation of atherosclerosis disease and the quantification of the risk of stroke. This requires the extraction of the intima-media complex (IMC), the delineation of the lumen the atherosclerotic carotid plaque and measurement of the artery stenosis. The current research proposes an automated segmentation system for the complete segmentation of the CCA bifurcation in ultrasound images, which is based on snakes. The algorithm was evaluated on 20 longitudinal ultrasound images of the CCA bifurcation with manual segmentations available from a neurovascular expert. The manual mean±SD measurements were for the IMT: (0.96±0.22) mm, lumen diameter: (5.59±0.84) mm and ICA origin stenosis (48.1±11.52) %, while the automated measurements were for the IMT: (0.93±0.22) mm, lumen diameter: (5.77±0.99) mm and ICA stenosis (51.05±14.51) % respectively. We found no significant differences between all manual and the automated segmentation measurements.

Segmentation of atherosclerotic carotid plaque in ultrasound video

The degree of stenosis of the common carotid artery (CCA) but also the characteristics of the arterial wall including plaque size, composition and elasticity represent important predictors used in the assessment of the risk for future cardiovascular events. This paper proposes and evaluates an integrated system for the segmentation of atherosclerotic carotid plaque in ultrasound video of the CCA based on normalization, speckle reduction filtering (with the hybrid median filter) and parametric active contours. The algorithm is initialized in the first video frame of the cardiac cycle with human assistance and the moving atherosclerotic plaque borders are tracked and segmented in the subsequent frames. The algorithm is evaluated on 10 real CCA digitized videos from B-mode longitudinal ultrasound segments and is compared with the manual segmentations of an expert, for every 20 frames in a time span of 3-5 seconds, covering in general 2 cardiac cycles. The segmentation results are very satisfactory with a true negative fraction (TNF) of 79.3%, a true-positive fraction (TPF) of 78.12%, a false-positive fraction (FPF) of 6.7% and a false-negative fraction (FNF) of 19.6% between the ground truth and the presented plaque segmentations, a Williams index (KI) of 80.3%, an overlap index of 71.5%, a specificity of 0.88±0.09, a precision of 0.86±0.10 and an effectiveness measure of 0.77±0.09. The results show that integrated system investigated in this study could be successfully used for the automated video segmentation of the carotid plaque.

Manual and automated intima-media thickness and diameter measurements of the common carotid artery in patients with renal failure disease

The objective of this study was to investigate differences in intima-media thickness (IMT) and diameter (D) measurements of the common carotid artery (CCA) in ultrasound imaging in normal subjects and renal failure disease (RFD) patients. Manual measurements by two experts and automated segmentation measurements (based on snakes and active contour models (ACM)) were carried out on 73 normal subjects, and 80 RFD patients. Statistical analysis was carried out using the Wilcoxon rank-sum test at p<0.05. Results demonstrated that the mean IMT and D measurements were significantly higher for the RFD group versus the normal group. Moreover, there was no significant difference between the manual and automated measurements. The ACM segmentation was slightly more accurate than segmentation based on snakes. Further work is needed to validate these findings on a larger group of subjects.

Despeckle Filtering Toolbox for Medical Ultrasound Video

Ultrasound medical video has the potential in differentiating between normal and abnormal tissue and structure. Ultrasound imaging is used in border identification and texture characterisation of the atherosclerotic carotid plaque in the common carotid artery CCA, the identification and measurement of the intima-media thickness IMT and the lumen diameter that are very important in the assessment of cardiovascular disease. However, visual perception is reduced by speckle noise affecting the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for increasing the visual quality or as a pre-processing step for further automated analysis, such as the video segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound video sequences. In order to facilitate this analysis, the authors have developed a video analysis software toolbox based on MATLAB® that uses video despeckling, texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA videos. The proposed software, which is based on a graphical user interface GUI, incorporates video normalisation, 4 different despeckle filtering techniques DsFlsmv, DsFhmedian, DsFkuwahara and DsFsrad, 65 texture features, 11 quantitative video quality metrics and objective video quality evaluation. The software was validated on 10 ultrasound videos of the CCA, by comparing its results with quantitative visual analysis performed by two medical experts. It was shown that the filters DsFlsmv, and DsFhmedian improved video quality perception based on the experts assessment and the video quality metrics. It is anticipated that the system could help the physician in the assessment of cardiovascular video analysis. However, exhaustive evaluation of the despeckle filtering toolbox has to be carried out by more experts on more videos.

Despeckle filtering in ultrasound video of the common carotid artery

Noise reduction is essential for increasing the visual quality or as a preprocessing step for further automated analysis in video sequences and video coding. The objective of this work was to investigate four different video despeckle filtering techniques and evaluate them using visual assessment by two medical experts, texture features analysis, and video quality evaluation metrics. The four proposed video despeckle filtering techniques were evaluated on 10 ultrasound videos of the common carotid artery (CCA). The filters were applied on the whole video frame and in a selected by the user region of interest (ROI) which included the atherosclerotic carotid plaque. The despeckle filters were based on linear filtering (DsFlsmv), hybrid median filtering (DsFhmedian), nonlinear filtering (DsFkuwahara) and speckle reducing anisotropic diffusion (DsFsrad) filtering. Our results showed that, the best video despeckle filtering methods were the linear filter DsFlsmv, followed by the hybrid median filter DsFhmedian. Both filters improved the visual perception evaluation by experts and gave better texture and video quality metrics. Further work on a larger number of videos and by employing additional despeckle filtering techniques is required for the evaluation of video despeckle filtering methods on ultrasound videos of the CCA.

Evaluation of wound healing process based on texture analysis

Wound healing rate, remains an interesting and important issue, in which modern imaging techniques have not yet given a definitive answer. In order to guide better therapeutic interventions, a better understanding of the fundamental mechanisms driving tissue repair are required. The wound healing rate is primarily quantified by the rate of change of the wounds surface area. The objective of this study was to establish a standardised and objective technique to asses the progress of wound healing in foot by means of texture analysis. The methods of image pre-processing, segmentation and texture analysis together with visual experts evaluation were used to assess the wound healing process. A total of 40 digital images from ten different subjects with food wounds were taken every third day, for 12 days, by an inexpensive digital camera under variable lighting conditions. The images were intensity normalized, and wounds were automatic segmented using a snakes segmentation system. From the segmented wounds 15 different texture characteristics and 4 different geometrical features were extracted in order to identify features that quantify the rate of wound healing. We found texture characteristics that may indicate the progression of wound healing process. More specifically, some texture features increase (mean, contrast), while some other texture features decrease (entropy, sum of squares variance, sum average, sum variance) with the progression of the wound healing process. Some of these features were found to be significantly different in a specific time point and this could be used to indicate the rate of wound healing. No significant differences were found for all geometrical measures. The results of this study suggest that some texture features might be used to monitor the wound healing process, thus reducing the workload of experts, provide standardization, reduce costs, and improve the quality for patients. The simplicity of the method also suggests that it may be a valuable tool in clinical wound evaluation. Future work will incorporate additional texture and geometrical features for assessing the wound healing process in order to be used in the real clinical praxis.

Video segmentation of the common carotid artery intima media complex

The correct identification of the intima-media thickness (IMT) of the common carotid artery (CCA) walls has a high clinical relevance as it represents one of the most reliable predictor for future cardiovascular events. In this work we propose and evaluate an integrated system for the segmentation of the intima-media complex (IMC) and the lumen diameter in longitudinal ultrasound video of the CCA based on normalization, speckle reduction filtering (with a first order statistics filter) and snakes segmentation. The algorithm is initialized in the first video frame of the cardiac cycle by an automated initialization procedure and the borders of the far wall and near wall of the CCA are estimated. The IMC and the carotid diameter are then segmented automatically in the consecutive video frames for one cardiac cycle. The proposed algorithm was evaluated on 10 longitudinal ultrasound B-mode videos of the CCA and is compared with the manual tracings of a neurovascular expert, for every 20 frames in a time span of 3-5 seconds, covering in general 1-2 cardiac cycles. The algorithm estimated an IMTmean± standard deviation of (0.72±0.22) mm while the manual results were (0.70±0.19). The mean maximum and minimum diameter was (7.08±1.37) mm and (6.53±1.13) mm respectively. The results were validated based on statistical measures and univariate statistical analysis. It was shown that there was no significant difference between the snakes segmentation measurements and the manual measurements. The proposed integrated system could successfully segment the IMC in ultrasound CCA video sequences thus complementing manual measurements.

Advanced statistical and adaptive threshold techniques for moving object detection and segmentation

The current research project proposes advanced statistical and adaptive threshold techniques for video object detection and segmentation. We present new statistical adaptive threshold techniques to show the advantages, and how these algorithms overcome the limitations and the technical challenges for object motion detection. The algorithm utilizes statistical quantities such as mean, standard deviation, and variance to define a new adaptive and automatic threshold based on two-frame and three-frame differencing. The proposed algorithms were compared with classic statistical thresholding methods on a testing video for human motion detection, and the experimental results show the effectiveness of the algorithms. Furthermore this research shows an evaluation and comparison among all statistical and adaptive algorithms and proves the benefits of the proposed algorithm.

Full-automated system for the segmentation of the common carotid artery in ultrasound images

The full segmentation of the common carotid artery (CCA) in ultrasound images is important for the evaluation of the intima media thickness (IMT) and for the measurement of the artery stenosis which are considered to be the significant markers for the clinical evaluation of the risk of stroke. The current research proposes full-automated segmentation system for the segmentation of the CCA, which is based on an adaptive snake-contour segmentation algorithm. The CCA is segmented by the proposed algorithm into different distinct regions, namely the IMT, intima-media (IL), media-layer (ML), carotid plaque and lumen. The proposed method is automatically processing image normalization, binarization, adaptive hybrid median filter, and morphology prior the application of the snake segmentation algorithm. The mean and standard deviation of the IMT diameter in y-axis of the full-automatically segmented regions for the snakes-based and level-set method are 0.12 mm +/- 0.01 mm and 0.09 mm +/- 0.01 mm respectively in comparison with the ground truth IMT extracted from the manual clinical segmentation. The Wilcoxon rank sum test shows the significant improvements of the proposed method.