Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI.

Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

Confidence Region Identification and Contour Detection in MRI Image.

Tumour region extraction (RE) method identifies the area of interest in MR imaging as it also highlights tumour boundaries. Some other intensities are existing, they are not visible but have their existence in region, and this region is called growing region. Such region is to be tumour region. Due to the variation of intensities in MRI images, tumour visibility becomes uncleared. Tumour intensity variations (tumour tissues) mix with normal brain tissues. In the light of above circumstance, tumour growing region becomes challenge. The goal of work is to extract the region of interest with confidence. The objective of the study is to develop the region of interest of brain tumour MRI image method by using confidence score for identifying the variation of intensity. The significance of work is based on identification of region of interest (tumour region). Confidence score is measured through pattern of intensities of MRI image. Similar patterns of brain tumour intensities are identified. Each pattern of intensities is adjusted with certain scale, and then biggest blob is analysed. Various biggest area blobs are combined, and resultant biggest blob is formed. In fact, resultant area blob is a combination of different patterns. Each pattern is assigned with particular colour. These colours highlight the growing region. Further, a contour is detected around the tumour boundaries. With combination of region scale fitting and contour detection (CD), tumour boundaries are further separated from normal tissues. Hence, the confidence score (CS) is formed from CD. CS is further converted to confidence region (CR). Conversion to CR is performed though confidence interval (CI). CI is based on defined conditions. In such conditions, different probabilities are considered. Probability identifies the region. Source of region formation is pixels; these pixels highlight tumour core significantly. This CR is obtained through checking standard deviation and statistical evaluation using confidence interval. Hence, region-of-interest pixels are identifying the CR. CR is evaluated through 97% Dice over index (DOI), 94% Jacquard, MSE 1.24, and PSNR 17.45. Value of testing parameter from benchmark study was JI, DOI, and MSE, PSNR : JI was 31.5%, DOI was 47.3%, MSE was 2.5 dB, and PSNR was 40 dB. The parameters are measured for the complex images; contribution parameter classifies the mean pixel values and deviating pixel values, and the classification of the pixel value is like to be termed as intensities. Mentioned classification extracts the variation of intensity pixels accurately; then, algorithm is highlighting the region as compared to the normal tumour cells.

Effect of Strength Training Protocol on Bone Mineral Density for Postmenopausal Women with Osteopenia/Osteoporosis Assessed by Dual-Energy X-ray Absorptiometry (DEXA).

This study aims to introduce a resistance training protocol (6 repetitions × 70% of 1 maximum repetition (1RM), followed by 6 repetitions × 50% of 1RM within the same set) specifically designed for postmenopausal women with osteopenia/osteoporosis and monitor the effect of the protocol on bone mineral density (BMD) in the lumbar spine, assessed by dual-energy X-ray absorptiometry (DEXA). The subjects included in the study were 29 postmenopausal women (56.5 ± 2.8 years) with osteopenia or osteoporosis; they were separated into two groups: the experimental group (n = 15), in which the subjects participated in the strength training protocol for a period of 6 months; and the control group (n = 14), in which the subjects did not take part in any physical activity. BMD in the lumbar spine was measured by DEXA. The measurements were performed at the beginning and end of the study. A statistically significant increase (Δ% = 1.82%) in BMD was observed at the end of the study for the exercise group (0.778 ± 0.042 at baseline vs. 0.792 ± 0.046 after 6 months, p = 0.018, 95% CI [-0.025, -0.003]); while an increase was observed for the control group (Δ% = 0.14%), the difference was not statistically significant (0.762 ± 0.057 at baseline vs. 0.763 ± 0.059, p = 0.85, 95% CI [-0.013, 0.011]). In conclusion, our strength training protocol seems to be effective in increasing BMD among women with osteopenia/osteoporosis and represents an affordable strategy for preventing future bone loss.