Sentence examples for disease image from inspiring English sources

Using the multilevel encoding approach presented, the medical Brain disease image itself may be hidden inside.

The first important step in the automatic recognition of this disease, image segmentation, is considered to be the most critical step.

In this system, select a grey Brain disease image as a cover image and a secret message of patient information, which will be embedded in the cover image.

In Brain Huntington's disease image, it is 90.43 and 379.44 kilo bytes, which is 35 and 37 %, respectively, of the image size.

In Brain Huntington's Disease image, it is 84.49 and 379.14 kilo bytes, which is 32 and 36%%, respectively, of the Brain image size.

In this paper, a novel secret transmission scheme has been proposed using the notion of opacity with respect to a diagonal queue least significant bit substitution, which is an extremely effective alternative for transmitting secure medical records and patient's personal identification information along with the appropriate medical Brain disease image.

In the coming years, we expect a more widespread study and application of these probes with potential clinical translations, in the areas such as deep-tissue imaging, molecular diagnosis of the disease, image-guided delivery, and targeted nanoparticle drug delivery, monitoring disease progression and outcome of therapy.

In [9] the impact of distortion correction on the classification accuracy regarding celiac disease images has been investigated.

The work presented in [10] investigated the impact of distortions and interpolation artifacts caused by distortion correction methods on the accuracy of a classification of celiac disease images.

These findings are in line with our clinical study in patients with infectious or inflammatory disease, imaged with similar 99mTc-PEG-liposomes, labelled with 99mTc-hexamethylpropylene-amine-oxime (Dams et al, 2000).

Since the present study is restricted to celiac disease images only, we plan to conduct similar studies on other image databases as well (e.g. high-definition colonoscopic images) in order to be able to assess if the results shown are generic or restricted to specific types of imagery.