No abstract
Medical Image Processing is the technique of analysing medical image such as CT, MRI for diagnostic and treatment purpose. The role of image compression is vital in domain of tele medicine for storage and transmission of medical data. In this project transform domain based compression algorithm is employed for the efficient compression and transfer of medical data. The Discrete Cosine Transform (DCT) based fractal image compression algorithm is employed for real time CT medical image. The result is validated by performance metrics Peak Signal to Noise Ratio (PSNR) and compression ratio. The algorithm was developed in MATLAB 2010a.
Diabetes is the root cause for the visual problems of human, the medical treatment is highly efficient in protecting the vision loss for the diabetic patients. Vision test are encouraged for the patients on regular basis for early detection of vision problems. Automated diabetic retinopathy evaluation programs have been developed for the identification of patients with vision losing diabetic eye disease. The non diabetic persons are also encouraged to obtain the evaluation of eye for the prevention of blindness. The proposed system is an automatic detection and evaluation of the eye disease. The detection of micro aneurysms and hemorrhages in retinal image are the base for the automated classification of red lesion. It includes the following contributions a) Extraction of a set of Dynamic shape features b) Classification process for the differentiation of lesions and vessel segments. This method is examined with the fundus images in e-ophtha dataset obtained from the freely availed database called ADCIS. The preprocessing of input images was done by mean filter. The Morphological flooding is applied for the extraction of shape features. Finally, the classification was done using Random Forest (RF) classifier. The experimental results on dataset validate the efficiency of the proposed method in the detection of redlesions.
Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2D retinal fundus images is promising for low cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2D retinal fundus images. Methods: In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc. Results: The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-the-art methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods. Conclusion: The proposed method achieves good accuracy for glaucoma detection. Significance: The method has a great potential to be used for large-scale population-based glaucoma screening.
Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red-yellow-black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS-Memorial Health Center Wound Clinic (Worcester, MA) following an IRB (Institutional Review Board) approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.
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