Abstract-We describe a novel recording system for the acquisition of multicompression strain images of the human prostate in vivo. The force at the tip of an ultrasonic transrectal probe is measured continuously, and ultrasonic rf-images are acquired consecutively at specified levels of compression. The acquired image sequence is processed by conventional cross-correlation techniques to obtain time shift estimates and corresponding strain images. We present phantom measurements as well as in vivo results and discuss the advantages and restrictions of the proposed system.
The incidence of the prostate carcinoma is one of the highest cancer risks in men in the western world. Its position in cancer mortality statistics is also among the highest. The prostate carcinoma is only curable at an early stage. Therefore, early detection is extremely important. At an early stage the prostate carcinoma is limited to the prostate capsule and can hence be cured performing radical prostatectomy.The different types of diagnostics that are used today (digital rectal examination, transrectal ultrasound and PSA value analysis) lack reliability and are therefore not sufficient. Even a combination of these three methods is not sufficiently reliable.Diagnosis of the prostate carcinoma using multifeature tissue characterization in combination with ultrasound allows the detection of tumors at an early stage. Also biopsy guidance and planning can be improved. This results in reduced costs for cancer treatment.
I. METHODS
Data AcquisitionRadio-frequency (RF) ultrasonic echo data of the prostate is captured during the usual examination of the patient with standard ultrasound equipment (Kretz Combison 330, transrectal probe, 7.5 MHz center frequency). Patient compliance is high, as the new method does not extend the normal examination time when applying transrectal ultrasound and the system is operator-independent. The RF-data is directly transmitted to a PC, sampled at 33 MHz and 12 bits and subdivided into up to 1000 segments per prostate slice. Up to five datasets per patient are being recorded.
Parameter ExtractionUp to 40 parameters are calculated for each segment. The extracted parameters do not claim to be independent of the ultrasound equipment. The parameters used for classification are calculated from the frequency spectrum and from the time domain. Spectrum parameters are calculated after applying a Hamming window to the RF data, computing the Fourier transform and converting the resultant power spectrum to dB. The primary set of spectrum parameters consists of measures of backscatter calculated for the signal bandwidth (slope, axis intercept, midband value, integrated power and deviation of the linear regression spectrum fit [1][3][5]). Parameters of an attenuation model (multi narrow band method [2][7][8]) are also included in the system. The texture parameters consist of first and second order (Cooccurrence) parameters. Common cooccurrence parameters are calculated for different distances [3][6].Initial results have shown that only a combination of these different fields of descriptors leads to adequate classification results. During the preselection procedure of parameters for the training process of the system, parameter vectors that are highly dependent on each other are found and discarded using covariance matrix analysis. Parameter vectors that have a small influence on the classification procedure are found and discarded using single classification. During the preselection the number of parameters is reduced from 40 to 16 for both fuzzy inference systems. As the number of segments used in...
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