Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to some extent,however,it often causes the position offset of object contours.For the purpose of reducing over-segmentation to preserve the location of object contours,the watershed segmentation based on the hierarchical multi-scale modification of morphological gradient is proposed.Firstly,multi-scale morphological filtering was employed to smooth the original image.Then,the gradient image was divided into multi-levels by the volume of three-dimension topographic relief,where the lower gradient layers were further modifiedby morphological closing with larger-sized structuring-elements,and the higher layers with the smaller one.In this way,most local minimums caused by irregular details and noises can be removed,while region contour positions corresponding to the target area were largely preserved.Finally,morphological watershed algorithm was employed to implement segmentation on the modified gradient image.The experimental results show that the proposed method can greatly reduce the over-segmentation of the watershed and avoid the position offset of the object contours.
In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.
In order to improve the steady state performance,dynamic response and power factor of traditional power factor correction(PFC)digital control method and reduce the harmonic distortion of input current,a double closed loop active power factorcorrection(APFC)control method with feed-forward is proposed.Firstly,the small signal model of Boost PFC control systemis built and the system transfer function is deduced,and then the parameters of the main device with Boost topology is estimated.By means of the feed-forward,the system can quickly respond to the change in input voltage.Furthermore,the use ofvoltage loop and current loop can achieve input current and output voltage regulation Simulink modeling shows that this methodcan effectively control the output voltage in case of input voltage largely fluctuating,improve the system dynamic response abilityand input power factor,and reduce the input current harmonic distortion
In the traditional three-level space vector pulse width modulation(SVPWM)algorithm,the sector judgment is computationallycomplex since the sector is divided into triangles and hexagons.In addition,the switching frequency is high becausethe seven-segment switching sequence is adopted.For this reason,a new SVPWM control algorithm for three-level inverteris proposed,in which the sector judgment is simplified by dividing the sector into quasi hexagons?and the new four-segmentswitching sequence is adopted to reduce the switching frequency.Simulation results show that the total harmonic distortiongrows down with the switching frequency decreasing,moreover,the algorithm runtime is also decreased.
