One-dimensional Median Filter Research Articles

Improving situation recognition using endoscopic videos and navigation information for endoscopic sinus surgery.

Endoscopic sinus surgery (ESS) is widely used to treat chronic sinusitis. However, it involves the use of surgical instruments in a narrow surgical field in close proximity to vital organs, such as the brain and eyes. Thus, an advanced level of surgical skill is expected of surgeons performing this surgery. In a previous study, endoscopic images and surgical navigation information were used to develop an automatic situation recognition method in ESS. In this study, we aimed to develop a more accurate automatic surgical situation recognition method for ESS by improving the method proposed in our previous study and adding post-processing to remove incorrect recognition. We examined the training model parameters and the number of long short-term memory (LSTM) units, modified the input data augmentation method, and added post-processing. We also evaluated the modified method using clinical data. The proposed improvements improved the overall scene recognition accuracy compared with the previous study. However, phase recognition did not exhibit significant improvement. In addition, the application of the one-dimensional median filter significantly reduced short-time false recognition compared with the time series results. Furthermore, post-processing was required to set constraints on the transition of the scene to further improve recognition accuracy. We suggested that the scene recognition could be improved by considering the model parameter, adding the one-dimensional filter and post-processing. However, the scene recognition accuracy remained unsatisfactory. Thus, a more accurate scene recognition and appropriate post-processing method is required.

Defect Detection in Striped Images Using a One-Dimensional Median Filter

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

High-Speed Hardware Implementation of Fixed and Runtime Variable Window Length 1-D Median Filters

Nonlinear digital filters play an important role in digital signal processing applications. In this brief, a novel architecture is proposed for the hardware implementation of fixed and runtime variable window length one-dimensional median filters. In the proposed architecture, the maximum working clock frequency is almost independent of the median filter window length, whereas the hardware complexity is proportional to the number of samples in the window. This feature enables the construction of filters with relatively large window lengths with negligible reduction in the maximum clock frequency, whereas in previous architectures, the maximum clock frequency significantly drops as the window length is increased. The benchmark results show the efficiency of the proposed architecture in comparison with state-of-the-art techniques.

Preliminary Results for Estimating Pulse Transit Time Using Seismocardiogram1

Preliminary Results for Estimating Pulse Transit Time Using Seismocardiogram1

A Low-Power Architecture for the Design of a One-Dimensional Median Filter

This brief presents a low-power architecture for the design of a one-dimension median filter. It is a word-level two-stage pipelined filter, receiving an input sample and generating a median output at each machine cycle. The power consumption is reduced by decreasing the number of signal transitions in the circuit. This can be done by keeping the stored samples immobile in the window through the use of a token ring in our architecture. The experimental results have shown that, at the expense of some additional area cost, the power consumption can be successfully reduced.

Development of a noise reduction filter algorithm for pediatric body images in multidetector CT.

Recently, several types of post-processing image filter which was designed to reduce noise allowing a corresponding dose reduction in CT images have been proposed and these were reported to be useful for noise reduction of CT images of adult patients. However, these have not been reported on adaptation for pediatric patients. Because they are not very effective with small (<20 cm) display fields of view, they could not be used for pediatric (e.g., premature babies and infants) body CT images. In order to solve this restriction, we have developed a new noise reduction filter algorithm which can be applicable for pediatric body CT images. This algorithm is based on a three-dimensional post processing, in which output pixel values are calculated by multi-directional, one-dimensional median filters on original volumetric datasets. The processed directions were selected except in in-plane (axial plane) direction, and consequently the in-plane spatial resolution was not affected by the filter. Also, in other directions, the spatial resolutions including slice thickness were almost maintained due to a characteristic of non-linear filtering of the median filter. From the results of phantom studies, the proposed algorithm could reduce standard deviation values as a noise index by up to 30% without affecting the spatial resolution of all directions, and therefore, contrast-to-noise ratio was improved by up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of pediatric body CT images.

Adaptive postprocessors with DCT-based block classifications

In this paper, an adaptive postprocessor figured with discrete cosine transform (DCT)-based block classification to effectively remove the so-called blocky effect from compressed video sequences is proposed. The proposed DCT-based detection algorithms for both intraframes and interframes require much lower computation complexity than the spatial-domain approaches. In order to preserve the edge information, the adaptive postprocessor is also designed with a DCT-based edge detection mechanism such that a one-dimensional median filter can be adaptively adjusted to match with the edge orientation. Simulation results show that the proposed DCT-based detection algorithms accurately classifies smooth, edge, or nonsmooth blocks to help the adaptive postprocessor to effectively remove the blocky effect and sharply preserve the edge information.

Root Images of Median Filters

Median filters are frequently used in signal analysis because of their smoothing properties and their insensitivity with respect to outliers in the data. Since median filters are nonlinear filters, the tools of linear theory are not applicable to them. One approach to deal with nonlinear filters consists in investigating their root images (fixed elements or signals transparent to the filter). Whereas for one-dimensional median filters the set of all root signals can be completely characterized, this is not true for higher dimensional filters. In 1989, Dohler stated a result on certain root images for two-dimensional median filters. Although Dohlers results are true for a wide class of median filters, his arguments were not correct and his assertions do not hold universally. In this paper we give a rigorous proof of Dohlers results. Moreover, his approach is generalized to the d-dimensional case.

A bit-level systolic array for median filter

A bit-level systolic array for median filters based on the majority of the bit strings of input samples is presented. The majority function can be implemented by a 1-b odd/even transposition network, which is regular and can be designed easily. If each input sample is represented by r bits, the systolic array contains r processor elements and requires time complexity O(N+n+r-1) for a stream of N samples with window size n in a one-dimensional median filter. >

Complete classification of roots to one-dimensional median and rank-order filters

The set of roots to the one-dimensional median filter is completely determined. Let 2N+1 be the filter window width. It has been shown that if a root contains a monotone segment of length N+1, then it must be locally monotone N+2. For roots with no monotone segment of length N+1, it is proved that the set of such roots is finite, and that each such root is periodic. The methods used are constructive, so given N, one can list all possible roots of this type. The results developed for the median filter also apply to rank-order filters. >

The Output Distribution of Median Type Filters

The distribution of the output of the one-dimensional median filter is derived for several cases including the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</tex> th-order output distribution with any input distribution. This is then used in several illustrative examples of median filtering a signal plus white noise.

A Separable Median Filter for Image Noise Smoothing

This paper investigates some properties of the separable filter resulting from successive applications of a one-dimensional median filter on the rows and columns of an image. Although the output of this separable filter is not identical to the corresponding nonseparable two-dimensional median filter with a square window, its performance in image noise smoothing is close. In particular, its effectiveness in smoothing noise and its behavior with edges are characterized and compared with those of the two-dimensional median filter. It is shown that the separable filter has a much simpler implementation in real-time hardware (at video rates, for example).