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Objective: To determine the frequency of depression in patients with vitamin B12 deficiency. Methodology: This cross-sectional study was carried out from January 2020 to March 2021 on a total sample size of n=100 patients (age 49.84 ± 0.91 years) using a simple random sampling technique, visiting the medical department of the federal government polyclinic hospital, in Islamabad. Patients presenting with macrocytic anaemia secondary to vitamin B12 deficiency were included while the patients with previously diagnosed depression were excluded from the study. Serum vitamin B12 levels were measured through the ELISA technique. Patient Healthcare Questionnaire-9 (PHQ-9) was designed and presented to patients with vitamin B12 deficiency for depression assessment. Patients with PHQ-9 score ≤5 were rated as having no depression and the patients with score ≥5 were rated as having depression. Data were analyzed using GraphPad Prism and the correlation coefficient was measured through non-linear fit and linear regression curves while ordinary ANOVA (non-parametric) was applied for study variables. Results: The average level of vitamin B12 in the serum and PHQ-9 score were recorded as 158 ± 4.94 (pg/uL) and 5.85 ± 0.30, respectively. Vitamin B12 levels and PHQ-9 score depicted an inverse correlation, however, no association was found between gender and PHQ-9 score. Conclusion: Data depicted that the patients presenting low levels of vitamin B12 in their serum have shown corresponding high PHQ-9 scores which is an indicator of depression. However, depression prevails in all age groups without any discrimination of gender. [ABSTRACT FROM AUTHOR]

• A novel approach comprised of fuzzy intelligence and GMM has been proposed for segmentation of brain MRI and carotid artery ultrasound images. • It utilizes statistical, wavelet, and textural features. • Robustness of the approach has been evaluated at Gaussian and Rician noise levels. • Application of the proposed approach to plaque detection in real carotid artery ultrasound images is assessed. • Benefit for clinical evaluation of the disease in both asymptomatic and symptomatic individuals. In medical image analysis for disease diagnosis, segmentation is one of the challenging tasks. Owing to the inherited degradations in MRI improper segments are produced. Segmentation process is an important step in brain tissue analysis. Moreover, an early detection of plaque in carotid artery using ultrasound images may prevent serious brain strokes. Unfortunately, low quality and noisy ultrasound images are still challenges for accurate segmentation. The objective of this research is to develop a robust segmentation approach for medical images such as brain MRI and carotid artery ultrasound images. In this paper, a novel approach is proposed to address the segmentation challenges of medical images. The proposed approach employed fuzzy intelligence and Gaussian mixture model (GMM). It comprises two phases; firstly, incorporating spatial fuzzy c-means in GMM by exploiting statistical, texture, and wavelet image features. During model development, GMM parameters are estimated in presence of noise by EM algorithm iteratively. Utilizing these parameters, brain MRI images are segmented. In next phase, developed approach is applied to solve a real problem of carotid artery plaque detection using ultrasound images. The dataset of real patients annotated by radiologists has been obtained from Radiology Department, Shifa International Hospital Islamabad, Pakistan. For this, intima-media-thickness values are computed from the proposed segmentation followed by support vector machines for plaque classification (normal/abnormal). The obtained segmentation has been evaluated on standard brain MRI dataset and offers high segmentation accuracy of 99.2%. The proposed approach outperforms in term of segmentation performance range of 3–9% as compared to the state of the art approaches on brain MRI. Furthermore, the proposed approach shows robustness to various levels of Gaussian and Rician image noises. On carotid artery dataset, we have obtained high plaque detection rate in terms of accuracy, sensitivity, specificity, and F-score values of 98.8%, 99.3%, 98.0%, and 97.5% respectively. The proposed approach segments both modalities with high precision and shows robustness at Gaussian and Rician noise levels. Results for brain MRI and ultrasound images indicate its effectiveness and can be used as second opinion in addition to the radiologists. The developed approach is straightforward, efficient, and reproducible. It may benefit to improve the clinical evaluation of the disease in both asymptomatic and symptomatic individuals. [ABSTRACT FROM AUTHOR]