A Quantitative Investigation on the Effect of Edge Enhancement for Improving Visual Acuity at Different Levels of Contrast.

BACKGROUND: The major limitation in human vision is refractive error. Auxiliary equipment and methods for these people are not always available. In addition, limited range of accommodation in adult people when switching from a far point to a near point is not simply possible. In this paper, we are looking for solutions to use the facilities of digital image processing and displaying to improve visual acuity when using digital display devices. We quantitatively investigate the effect of edge enhancement on improving the visual acuity at different levels of contrast. We can improve visual acuity for people such as emmetropia, myopia and hyperopia when they utilize display devices. MATERIALS AND METHODS: According to the objective of this research, 24 visual acuity optical charts were designed using MATLAB software, based on logMAR standard. The charts have different levels of contrast with enhanced edges of optotypes at two brightness levels: 0 and 255. The proposed patterns were tested on 20 human subjects. The obtained results for each chart were analyzed in SPSS software. RESULTS: The results show that at all contrast levels, edge enhancement improves visual acuity. The degree of improvement where the edges have brightness level of 0 is higher than where the edges have brightness level of 255. CONCLUSION: Based on the results, enhancing the edges of optotypes in the background image improves visual acuity by about 16.1% on logMAR scale.

Automatic diagnosis of vulvovaginal candidiasis from Pap smear images.

Vulvovaginal candidiasis (VVC) is the most common genital infections that are seen every day in clinics. This infection is due to excessive growth of Candida that are normally present in the vagina in small numbers. Diagnosis of VVC is routinely done by direct microscopy of Pap smear samples and searching for the Candida in the Pap smear glass slides. This manual method is subjective, time consuming, labour-intensive and tedious. This study presents a computer-aided diagnostic (CAD) method to improve human diagnosis of VVC. The proposed CAD method reduces the diagnostic time and also can be worked as a second objective opinion for pathologists. Our main objective is detection and extraction of mycelium and conidium of Candida fungus from microscopic images of Pap smear samples. In this regard, the proposed method is composed of three main phases, namely preprocessing, segmentation, feature extraction and classification. At the first phase, bottom-hat filtering is used for elimination of the cervical cells and separating the background. Then decorrelation stretching and colour K-means clustering are used for Candida segmentation. Finally the extracted features used by a decision tree classifier to detect Candida from other parts of smear. The proposed method was evaluated on 200 Pap smear images and showed specificity of 99.83% and 99.62% and sensitivity of 92.18% and 94.53% for detection of mycelium and conidium, respectively.

Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier.

Plasma cells are developed from B lymphocytes, a type of white blood cells that is generated in the bone marrow. The plasma cells produce antibodies to fight with bacteria and viruses and stop infection and disease. Multiple myeloma is a cancer of plasma cells that collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. The definitive diagnosis of multiple myeloma is done by searching for myeloma cells in the bone marrow slides through a microscope. Diagnosis of myeloma cells from bone marrow smears is a subjective and time-consuming task for pathologists. Also, because of depending on final decision on human eye and opinion, error risk in decision may occur. Sometimes, existence of infection in body causes plasma cell's increment which could be diagnosed wrongly as multiple myeloma. The computer diagnostic process will reduce the diagnostic time and also can be worked as a second opinion for pathologists. This study presents a computer-aided diagnostic method for myeloma cells diagnosis from bone marrow smears. At first, white blood cells consist of plasma cells and other marrow cells are separated from the red blood cells and background. Then, plasma cells are detected from other marrow cells by feature extraction and series of decision rules. Finally, normal plasma cells and myeloma cells could be classified easily by a classifier. This algorithm is applied on 50 digital images that are provided from bone marrow aspiration smears. These images contain 678 cells: 132 normal plasma cells, 256 myeloma cells and 290 other types of marrow cells. Applying the computer-aided diagnostic method for identifying myeloma cells on provided database showed a sensitivity of 96.52%; specificity of 93.04% and precision of 95.28%.