Effects of discrete wavelet compression on automated mammographic shape recognition

At present early detection is critical for the cure of breast cancer. Mammography is a breast screening technique which can detect breast cancer at the earliest possible stage. Mammographic lesions are typically classified into three shape classes, namely round, nodular and stellate. Presently this classification is done by experienced radiologists. In order to increase the speed and decrease the cost of diagnosis, automated recognition systems are being developed. This study analyses an automated classification procedure and its sensitivity to wavelet based image compression; In this study, the mammographic shape images are compressed using discrete wavelet compression and then classified using statistical classification methods. First, one dimensional compression is done on the radial distance measure and the shape features are extracted. Second, linear discriminant analysis is used to compute the weightings of the features. Third, a minimum distance Euclidean classifier and the leave-one-out test method is used for classification. Lastly, a two dimensional compression is performed on the images, and the above process of feature extraction and classification is repeated. The results are compared with those obtained with uncompressed mammographic images

Human Psychology Factors Influencing Agile Team Autonomy in Post-Pandemic Remote Software Organizations

Agile project management methods are gaining in popularity in the software industry as software development teams are being asked to be adaptive to market needs and resilient to change and uncertainty. With increasing market uncertainty, global competition, and time-to-market pressure, it is becoming a challenge to develop an innovative product and deliver it on-time without the opportunity that comes from team autonomy to experiment and learn from failures in a remote workplace. To resolve this challenge, it is critical to understand the myriad human psychological factors in play that influence Agile team autonomy in a remote work environment. The role of human psychological factors on Agile project delivery success has been largely neglected or superficially covered in extant literature. The purpose of this research study was to study the influence of key human psychological factors on emergence of Agile team autonomy that leads to Agile project success in software organizations. The findings will help Information Systems researchers and practitioners in proactively identifying and addressing human psychology factors challenges to achieve successful delivery of innovative products using Agile Scrum methodology. Using an online survey instrument, the study sampled 137 software professionals from US software companies with experience in the Agile Scrum role of Team Member. The quantitative data generated was analyzed using multiple linear regression. The relationship between the independent variables – the human psychology factors pertaining to Leadership Style, Organization Structure, HR Practices and Stakeholder Engagement and the dependent variable - Agile team autonomy is explained through multiple linear regression. As multiple items are linked to variables, the statistical analysis was performed using the median scores for each variable. One-way ANOVA and Pearson’s correlation coefficient were used to demonstrate the existence (or nonexistence) of relationships between variables. Finally, an empirical model relating the human psychology factor variables and the dependent variable of Agile team autonomy was constructed for the population

Analysis of the Contribution of Scale in Mammographic Mass Classification

Mammographic masses are often classified according to their shape as round, nodular or stellate. These classifications are useful in the recognition of benign vs. malignant masses. In this preliminary study, a set of 30 mammograms are analyzed. The 2D shape contour of each mass is mapped to a 1D radial distance measure. The discrete wavelet transform is applied to each measure in which a full decomposition is computed. The root-mean-square of the coefficients in each scale is then computed. These values are used as input features in a statistical classification system. The discriminating powers of these features are analyzed via linear discriminant analysis. The classification system utilizes a conventional Euclidean distance measure to determine class membership. The classification rates are 87%, 93% and 80% when using Haar, Daubechies-3 and Daubechies-5 wavelets, respectivel