Median Filter For Transition Region Refinement In Image Segmentation

Transition region based image segmentation is one of the simple and effective image segmentation methods. This method is capable to segment image contains single or multiple objects. However, this method depends on the background. It may produce a bad segmentation result if the gray level variance is high or the background is textured. So a method to repair the transition region is needed. In this study, a new method to repair the transition region with median filter based on the percentage of the adjacent transitional pixels is proposed. Transition region is extracted from the grayscale image. Transition region refinement is conducted based on the percentage of the adjacent transitional pixels. Then, several morphological operations and the edge linking process are conducted to the transition region. Afterward, region filling is used to get the foreground area. Finally, image of segmentation result is obtained by showing the pixels of grayscale image that are located in the foreground area. The value of misclassification error (ME), false negative rate (FNR), and false positive rate (FPR) of the segmentation result are calculated to measure the proposed method performance. Performance of the proposed method is compared with the other method. The experimental results show that the proposed method has average value of ME, FPR, and FNR: 0.0297, 0.0209, and 0.0828 respectively. It defines that the proposed method has better performance than the other methods. Furthermore, the proposed method works well on the image with a variety of background, especially on image with textured background

Development of transition region based methods for image segmentation

In this thesis, some transition region based segmentation approaches have developed to perform image segmentation for grayscale and colour images. In computer vision and image understanding applications, image segmentation is an important pre-processing step. The main goal of the segmentation process is the separation of foreground region from background region. The segmentation approaches are application specific and do not work well for both grayscale and colour image segmentation. For any image consisting of foreground and background, some transition regions exist between the foreground and background regions. Effective extraction of transition region leads to a better segmentation result. Therefore, the doctoral thesis intends to efficient and effective transition region extraction approaches for image segmentation for both grayscale and colour images

An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment

Real-Time Automatic Linear Feature Detection in Images

Linear feature detection in digital images is an important low-level operation in computer vision that has many applications. In remote sensing tasks, it can be used to extract roads, railroads, and rivers from satellite or low-resolution aerial images, which can be used for the capture or update of data for geographic information and navigation systems. In addition, it is useful in medical imaging for the extraction of blood vessels from an X-ray angiography or the bones in the skull from a CT or MR image. It also can be applied in horticulture for underground plant root detection in minirhizotron images. In this dissertation, a fast and automatic algorithm for linear feature extraction from images is presented. Under the assumption that linear feature is a sequence of contiguous pixels where the image intensity is locally maximal in the direction of the gradient, linear features are extracted as non-overlapping connected line segments consisting of these contiguous pixels. To perform this task, point process is used to model line segments network in images. Specific properties of line segments in an image are described by an intensity energy model. Aligned segments are favored while superposition is penalized. These constraints are enforced by an interaction energy model. Linear features are extracted from the line segments network by minimizing a modified Candy model energy function using a greedy algorithm whose parameters are determined in a data-driven manner. Experimental results from a collection of different types of linear features (underground plant roots, blood vessels and urban roads) in images demonstrate the effectiveness of the approach

A signal complexity-based approach for AM–FM signal modes counting

I segnali modulati in frequenza appaiono in molte discipline applicate, tra cui la geologia, la comunicazione, la biologia e l'acustica. Questi sono multicomponenti, cioè consistono in forme d'onda multiple, con frequenza specifica dipendente dal tempo (frequenza istantanea). Nella maggior parte delle applicazioni pratiche, il numero di modalità - che è sconosciuto - è necessario per analizzare correttamente un segnale; per esempio per separare ogni singolo componente e per stimare la sua frequenza istantanea. Il rilevamento del numero di componenti è un problema impegnativo, specialmente nel caso di modalità che interferiscono. L'approccio basato sull'entropia di Rényi si è dimostrato adatto per il conteggio delle modalità di un segnale, ma è limitato a componenti ben separate. Il presente documento affronta questo problema introducendo una nuova nozione di complessità del segnale. In particolare, lo spettrogramma di un segnale multicomponente è visto come un processo non stazionario in cui l'interferenza si alterna alla non interferenza. La complessità relativa alla transizione tra sezioni consecutive dello spettrogramma viene valutata mediante la Run Length Encoding. Sulla base di una legge di evoluzione tempo-frequenza dello spettrogramma, le variazioni di complessità sono studiate per stimare accuratamente il numero di componenti. Il metodo presentato è adatto a segnali multicomponente con modalità non separabili, così come ad ampiezze variabili nel tempo e mostra robustezza al rumore.Frequency modulated signals appear in many applied disciplines, including geology, communication, biology and acoustics. They are naturally 1multicomponent, i.e., they consist of multiple waveforms, with specific time-dependent frequency (instantaneous frequency). In most practical applications, the number of modes—which is unknown—is needed for correctly analyzing a signal; for instance for separating each individual component and for estimating its instantaneous frequency. Detecting the number of components is a challenging problem, especially in the case of interfering modes. The Rényi Entropy-based approach has proven to be suitable for signal modes counting, but it is limited to well separated components. This paper addresses this issue by introducing a new notion of signal complexity. Specifically, the spectrogram of a multicomponent signal is seen as a non-stationary process where interference alternates with non-interference. Complexity concerning the transition between consecutive spectrogram sections is evaluated by means of a modified Run Length Encoding. Based on a spectrogram time-frequency evolution law, complexity variations are studied for accurately estimating the number of components. The presented method is suitable for multicomponent signals with non-separable modes, as well as time-varying amplitudes, showing robustness to noise