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Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics
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Image of FIG. 1.
FIG. 1.

Integrated intensity of B-mode frames pre and post image registration. The intensity of each pixel was integrated over all frames within video 2 (a) pre and (b) post image registration. With the integrated intensity algorithm, motion within the videos should cause features to blur. Although video 2 had the greatest percent decrease in sum of squared difference scores after motion correction (Table III), the two integrated intensity images looked nearly identical; features can be seen sharply in both images.

Image of FIG. 2.
FIG. 2.

(Color online) Time-intensity curve preprocessing. Sample processing of the compressed video signal from a single representative pixel from a typical (a) benign and (b) malignant video. TICs for each pixel were either low pass filtered (LPF) or the envelope curve (EC) was detected prior to linearization. LPF allowed characterization of the overall wash-in and wash-out perfusion patterns, while EC and the unprocessed signal allowed detection of transient fluctuations in concentration of the contrast agent.

Image of FIG. 3.
FIG. 3.

(Color online) Performance of LDA classifier. Histograms showing results of the LDA classification of five variables in LDA space. The top combination from each of the groups are presented in the order of increasing Fisher discriminant criterion, which quantifies the separation of the classes by the separation of the means normalized by the sum of the standard deviations: (a) nonpixel-by-pixel (18.8); (b) mean based (35.8); (c) standard deviation based (38.4); (d) pixel-by-pixel (106); and (e) all variables (281). Note the differences in the scale of the x-axis. The combinations of variables chosen for the five groups were (a) non-P × P (AUCWO_M, MTT_M, WOT80_M, AUOC_M, TOA_M); (b) mean-based (MTT_M, WOT50_M, DIWO15_M, ISDN_M, AUEC_M); (c) SD-based (FWHM_S, MTT_S, WOT50_S, OEDNM_S, TOA_S); (d) P × P (PGWI_S, ISDN_S, PGWONM_S, PSWINM_S, coverage); (e) all (FWHM_M, PW_M, WOT80_M, OEDN_S, coverage). AUCWO: area under the wash-out curve; MTT: mean transit time; WOT50, 80: wash-out time to 50 and 80% of peak; AUOC: area under the original TIC curve; TOA: time of arrival; DIWO15: drop of intensity wash out to 15 s; ISDN: standard deviation of intensity normalized to peak; AUEC: area under the envelope curve; FWHM: full width at half maximum; OEDNM: envelope curve difference normalized to the mean of the peak frame; PGWI: peak gradient wash-in; PGWONM: peak gradient wash-out normalized to the peak frame; PSWINM: peak slope wash-in normalized to peak frame; PW: peak width; _M: mean within the ROI; _S: standard deviation within the ROI.

Image of FIG. 4.
FIG. 4.

(Color online) Correlation of top combination of variables. This figure shows the correlations of the five variables that provided the best discrimination of benign and malignant tumors according to the Fisher discriminant criterion. Out of the five variables, three pairs of variables were highly correlated with each other, while the remaining seven pairs were predominantly uncorrelated. FWHM: full width at half maximum; PW: peak width; WOT80: wash-out time to 80% of peak; OEDN: envelope curve difference normalized to the mean of the peak frame; _M: mean with the ROI; _S: standard deviation with the ROI.

Image of FIG. 5.
FIG. 5.

Enhancement differences due to manual bolus injections. (a) Peak enhancement frame from video 15 (rat 8, BR38). (b) Peak enhancement frame from video 16 (rat 8, BR55). These two images were acquired approximately 30 min apart following the same bolus injection and imaging protocol. In video 15, the significantly reduced enhancement was most likely due to operator error during injection of microbubbles.


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Animal ID, histology of the tumor, video ID, contrast agent used, and targeting of the contrast agents are listed.

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This table lists the kinetic measurements employed to parameterize the TIC, their acronyms, and the signal processing step(s) for each variable, and the effect of pixel-by-pixel measurement on the mean of each measurement. Throughout this report, variable acronyms suffixed with “_m” or “_s” indicate the mean or standard deviation of the pixel-by-pixel measurements within the ROI, respectively.

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Total sum of squared differences (SSD) from frame to frame of B-mode intensities within the tumor region of interest pre- and post-image registration. Due to anaesthetization and immobilization of the rats and US probe, there was little motion within the video other than minor breathing motion. Image registration lowered the SSD values, indicating reduced motion.

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Cross-validation error rates from linear discriminant analysis classification of benign and malignant tumors using combinations of one or two variables. With as few as two variables, a 0% cross-validation error rate was achieved within the small dataset of 19 videos.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automating tumor classification with pixel-by-pixel contrast-enhanced ultrasound perfusion kinetics