in this paper, we report the image processing technique for computer-aided diagnosis of lung cancer screening system by CT (LSCT). LSCT is the newly developed mobile-type CT scanner for the mass screening of lung cancer by our project team. in this new LSCT system, one essential problem is the increase of image information to be diagnosed by a doctor to about 30 slices per patient from I X-ray film. To solve this difficult problem, we are trying to reduce the image information drastically to be displayed for the doctor by image processing techniques. We propose a new method named Variable New-Quoit filter for the automatic recognition of the pathological shadow candidates. Our computer aided diagnosis (CAD) system can satisfactoiily reduce the number of CT cross sections by this method, containing the abnormal shadow candidates.
SUMMARYThe authors have developed the quoit filter, which is a kind of mathematical morphological filter, for automatic extraction of candidate pathological areas of lung cancer. The method has problems, however, in processing speed or extraction accuracy. To overcome these problems, this paper proposes variable quoit filtering, in which the filter size is adjusted flexibly according to the pathological shadow, and distance transformation with gray-level weight is applied as preprocessing before the main filtering procedure. First, the performance of the method is analyzed using a model, and the effectiveness of the proposed method is shown. Then, trial applications to images of 82 actual cases (including 21 cancer areas) show that all of the cancer areas were correctly extracted. Compared to the conventional algorithm, the processing time is reduced to less than 1/20.
This paper proposes a new method for the pitch estimation of sung songs for transcription. The previous pitch estimation methods that are considered most for musical instrument sounds but few for sung songs are based on the extraction of the pitch frequencies. But the extraction of the pitches is rather difficult and sophisticated signal processing is necessary since the musical sound has many harmonic components. The principle of our method is the elimination of the pitch and its harmonic frequencies. This can be performed simply by a comb filter. To adapt the fluctuation of song pitches, we use double comb filters. Using two cascade connected twelve double comb filters that are connected in parllel and are corresponding to each tone in one octave, we can estimate the pitches of solo and duet songs.
In this paper, we propose a method of the automatic transcription system remarking zero outputs of comb filters. In the frequency domain, the comb filter has zero points at the integer multiple of the fundamental frequency. The fundamental frequency and harmonic frequencies of the musical tone can be eliminated by comb filters which are corresponding to the scales. In the method using comb filters which has already presented, 60 comb filters are required, and the processing time is too long to detect the scale of the 32th note. In the proposed method,we can estimate the scale of both monophony and chords using only seven comb filters and a few notch filters connected in cascade, and we can also detect the scale of the 32th note(62.5 ms). We have done some computer simulations on transcribing the melody played on a piano. As a result, it is clear that the proposed method is useful to detect musical scale.
SUMMARYPitch estimation processing is an essential process in the automatic scoring of music. In this paper we propose a method for estimating the pitch of polyphonic vocal lines, an area that has received almost no research. The proposed method estimates pitch by using 12 comb filters connected together in parallel (corresponding to the 12 notes of an octave) together with singular value decomposition (SVD). The approach is based on the fact that a comb filter (H(z) = 1 -z −N ) is able to eliminate all the harmonic components of the note to which it corresponds and also that the SVD of the matrix of signal samples enables us to distinguish the number of harmonic components in the signal from the number of singular values obtained. In other words, together with the above-mentioned structure we are able to use the SVD to detect outputs in which the number of harmonic components has been reduced and thereby estimate the pitch of a note. We confirm the effectiveness of the proposed method by presenting the results of estimating pitch using the proposed method on polyphonic vocal music with both two and three voices singing at the same time.
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