No abstract

Thermography of the breast has been shown to be well suited to detect early signs of breast cancer. This study proposes a novel method for boundary detection of breast thermography images using directional SUSAN method. Among breast thermography image processing steps, breast isolation from background and from each other is an essential stage for proper detection of breast cancer. For this purpose, in this study, breast boundary is grouped into three regions depending on the region property. The algorithm of boundary detection is different for each region. Specially, for bottom breast boundary, directional SUSAN edge detector is presented that uses two rectangle masks to create a directional SUSAN gradient image with emphasis on oblique useful edges and omitting undesirable ones. Then cubic parabolic interpolation is implemented to determine a set of edge points on the boundaries. At last, an effective search algorithm is executed to correct some false points in order to extract breast boundaries accurately. The performance of the proposed approach illustrated by applying on the images of three databases. Experimental results show that this method acts effectively and confirm the accurate boundary detection. Moreover, statistical measures are calculated to indicate the remarkable capabilities of the proposed approach.

Various level‐set methods have been suggested for segmenting images with intensity inhomogeneity as local region‐based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region‐based active contour model called local self‐weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well‐known local level‐set methods which show the perfect capability of the model. The segmentation results were evaluated using the F‐score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F‐score, precision and recall criteria on the segmentation of breast thermography images, respectively.

No abstract

This paper presents an approach for model extraction, formal specification, verification and repair of the scheduler of Contiki, which is an event-driven lightweight Operating System for the Internet of Things (IoT). We first derive a state machine-based abstraction of the scheduler’s modes of operation along with the control flow abstractions of the scheduler’s most important functions. We then use a set of transformation rules to formally specify the scheduler and all its internal functions in Promela. Additional contributions with respect to the conference version of this article include (1) modeling nested function calls in the Promela model of the scheduler using a novel technique amenable to model checking in SPIN; (2) modeling protothreads in Promela; (3) specifying and formally verifying twelve critical requirements of the scheduler; (4) detecting new design flaws in Contiki’s scheduler, for the first time (to the best of our knowledge); (5) repairing the model and the source code of Contiki’s scheduler towards fixing the flaws detected through verification, as well as regression verification of the entire model of the scheduler, and (6) experimentally analyzing the time and space costs of verification before and after repair. The proposed formal model of Contiki’s scheduler along with novel modeling techniques enhance our knowledge regarding the most critical components of Contiki, and provide reusable methods for formal specification and verification of other event-driven operating systems used in Cyber Physical Systems (CPS) and IoT.