Quality of life assessment in patients with chronic rhinosinusitis by using rhinosinusitis disability index

Background: Chronic rhinosinusitis is a common disease affecting the nose and paranasal sinuses. One of the commonly used indices to assess the impact of chronic rhinosinusitis on quality of life is the rhinosinusitis disability index (RSDI). Objective was to study the surgical techniques and surgical findings of all patients with chronic rhinosinusitis undergoing endoscopic sinus surgery and to compare pre-surgical with post-surgical RSDI score. Methods: The patients were given RSDI questionnaire forms which contain questions regarding physical, functional and emotional factors which will be answered before and 3 months after endoscopic sinus surgery. The surgical technique and surgical findings of all patients undergoing the surgery are studied. The pre-surgical and post-surgical RSDI scores are compared and analysed by the Wilcoxon sign rank test. Results: Uncinectomy and middle meatal antrostomy were performed in all the patients undergoing functional endoscopic sinus surgery. Wilcoxon sign rank test is done to assess the difference in the pre and post-operative RSDI score and found to be statistically significant with p value <0.001 which indicates improvement in the RSDI score following functional endoscopic sinus surgery. Conclusions: RSDI is a valuable tool in assessing the health-related quality of life in patients with chronic rhinosinusitis

A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

&lt;p class="0abstract"&gt;Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.&lt;/p&gt;</jats:p

A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR

Data Search Rank Extortion and Malware Identification in Google Play

The Google Play-the chief boundless computerization utility advertises in which rank maltreatment and malware appearance has multiplied quick. In this paper, we tend to present Fair play, an first rate device that unearths and pursues malware deserted by means of fraudsters. The proposed framework's point is to find malware and programs exposed to seek rank misrepresentation. Fair play associates audit physical games and unambiguously consolidates diagnosed audit relations with etymological and behavior signals acquired from Google Play utility statistics. Fair play accomplishes nice excellent level datasets of cross for expansive peruse through making use of a few technique to every software to test its positioning. Our want is to make a immaculate, misrepresentation less utility.Fraudsters make extortion by downloading application through various gadgets and give extortion evaluations and audits. Along these lines, we keep an eye on previously mentioned to mine critical data relating to explicit application through audits that are obtained from remarks. Afterward, these audits are joined to mine extortion in application positioning.</jats:p