Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Gray matter structural correlates of fatigue in multiple sclerosis

We aimed to assess whether frontal cortex-striatum-thalamus (FCST) pathway or other grey matter (GM) structures are associated with longitudinal patterns of fatigue, namely reversible (RF) versus sustained fatigue (SF). MS patients enrolled in our prospective cohort were grouped based on their longitudinal Modified Fatigue Impact Scale (MFIS) scores: 1. SF: MFIS≥38 at the two most recent yearly assessments; 2. RF: MFIS<38 at last assessment, but presence of at least one previous MFIS≥38; 3. Never Fatigued (NF): at least five MFIS<38. Accordingly, we selected 98 patients (30 SF, 31 RF, 37 NF; age-range:29-66, female/male:76/22, Extended Disability Status Scale (EDSS)6; 13 patients with secondary progressive (SP) MS and 85 with relapsing remitting (RR) MS in remission). Disability and depression were assessed using the EDSS and CES-D, respectively. 3T T1-weighted MRI was used for voxel based morphometry (VBM) to survey for GM atrophy associated with fatigue, controlling for age, sex and EDSS. Group-wise volumetric comparison was performed on deep GM structures identified by VBM, controlling for age, sex, EDSS and CES-D score. VBM showed significant inverse relation between the MFIS cognitive subscale score and areas within the bilateral fronto-medial and fronto-orbital cortices, anterior striata, thalami, temporal poles, insulae and left lateral occipital cortex (peak FWE-p value of 0.021), and between the MFIS physical subscale and areas within the bilateral frontal poles, and frontal medial cortices (peak FWE-p value of 0.043). Volumetric analysis showed significant atrophy in the putamen (RF<NF p<0.0004; SF<NF p<0.0085) and thalamus (RF<NF p<0.00048)

ROI-based reversible watermarking scheme for ensuring the integrity and authenticity of DICOM MR images

Reversible and imperceptible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back. In this paper, a novel blind reversible watermarking approach is presented to detect intentional and unintentional changes within brain Magnetic Resonance (MR) images. The scheme segments images into two parts; the Region of Interest (ROI) and the Region of Non Interest (RONI). Watermark data is encoded into the ROI using reversible watermarking based on the Difference Expansion (DE) technique. Experimental results show that the proposed method, whilst fully reversible, can also realize a watermarked image with low degradation for reasonable and controllable embedding capacity. This is fulfilled by concealing the data into ‘smooth’ regions inside the ROI and through the elimination of the large location map required for extracting the watermark and retrieving the original image. Our scheme delivers highly imperceptible watermarked images, at 92.18-99.94dB Peak Signal to Noise Ratio (PSNR) evaluated through implementing a clinical trial based on relative Visual Grading Analysis (relative VGA). This trial defines the level of modification that can be applied to medical images without perceptual distortion. This compares favorably to outcomes reported under current state-of-art techniques. Integrity and authenticity of medical images are also ensured through detecting subsequent changes enacted on the watermarked images. This enhanced security measure, therefore, enables the detection of image manipulations, by an imperceptible approach, that may establish increased trust in the digital medical workflow

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

&lt;p&gt;Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.&lt;/p&gt; &lt;p&gt;Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.&lt;/p&gt; &lt;p&gt;Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.&lt;/p&gt

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

PENINGKATAN KUALITAS RADIOGRAF PERIAPIKAL PADA DETEKSI PULPITIS MENGGUNAKAN ADAPTIVE REGION GROWING APPROACH

Teeth are an important organ that has the function of chewing, talking, and aesthetics. Each person won’t be able to move normally if the dental injured. Pulpitis dental disease, namely inflammation in dental pulp that cause pain are divided into two types, reversible pulpitis and irreversible pulpitis. Available facilities in Indonesia to receive treatment for dental problems is still limited, especially in rural areas. Pulpitis can be detected with the help of periapical radiographs, the results of X-ray of the tooth which has the cheap cost, so it can be affordable for the lower middle class society. However, these results have low contrast levels that cause disease detection becomes difficult. Adaptive Region Growing Approach is used in this study to improve the image quality, wherein the method is more focused on improving the quality of the region that are created because of the seed in the image. The study produced periapical radiograph images that have better quality, that are expected to help dentists in detecting pulpitis, so it may indirectly increase the level of social welfare. These results were obtained through comparison of CII (Contrast Improvement Index) and SNR (Signal to Noise Ratio) between this method and the existing method