Visualizando Gammagramas Óseos en Colores

Abstract

RESUMENEn este trabajo se describe el uso de un software para detectar metástasis óseas en gammagramas cuyo funcionamiento está basado en un método cuantitativo. En la investigación se incluyeron cuarenta y tres gammagramas óseos de pacientes con cáncer de próstata, los cuales fueron previamente analizados visualmente por tres especialistas y su diagnóstico se tomó como referencia. Debido a que no todos los huesos de un paciente muestran los mismos tonos de gris, cada uno de los gammagramas se segmentó para su análisis en cuatro regiones: cráneo, hombros, tórax y pelvis. La segmentación se hizo con el fin de obtener intervalos de tonos de gris en cada una de las regiones empleando un proceso estadístico. Para ello se calcularon la media y la desviación estándar de cuatro muestras que contenían cada una cuarenta y tres regiones. Tomando en consideración los intervalos, siete colores fueron asignados a cada región. Mediante los colores fue posible diferenciar los casos sanos e infiltrados en cada región, lo que facilita el diagnóstico. Se muestran ejemplos de los resultados en cada una de las regiones.PALABRAS CLAVE: gammagrama óseo; metástasis óseas; cáncer de próstata; procesado digital de imágenes ABSTRACTThe use of software based on a quantitative method for detecting skeletal metastasis in bone scans is presented. Forty-three bone scans of patients with prostate cancer were previously analyzed visually by three specialists and their diagnosis were taken as a reference. Later, these scans were analyzed by segmenting them into four regions: skull, shoulders, chest, and pelvis. The segmentation was made to obtain intervals of gray levels for each of the regions using a statistical process. These values were found by calculating the mean and standard deviation of four samples containing each forty-three regions. Using these intervals, seven colors were assigned to each region. By means of the colors it is possible to differentiate healthy and infiltrated cases in each region, which may facilitate the diagnosis. Examples of results in each region are shown.

System for medical image visualization: SYFANMI

“…The previously mentioned functions were tested separately and the results are published in references 10,11,[12][13][14][15]17,18 , where we emphasize the collaboration with the Centro Medico Nacional La Raza (CDMX). In such a center, 150 bone scans were obtained following the ethical protocols established by the IMSS, from which 35 were thoroughly analyzed for these functions 17, and the results were compared with their corresponding medical diagnosis.…”

“…An application of the function is shown 14,15 where an analysis by lines in an image of a humeral head, obtained from a photographic process of flat radiography, helps the user quantify the observed bone degradation due to early metastatic disease. The first graph (a) in Fig.…”

A software tool based on quantitative methods for the analysis of bone gammagrams

“…For a given sample of patients (as for different ROIs) such values will form an interval since every patient's bone will not display the same gray tones. We performed the segmentation of each ROI intending to obtain the different gray tones intervals, considered for the assignation of the seven colors (false color), assigning different colors for each region and hence differentiating healthy cases from infiltrated ones for ROI, easing the diagnostic 7 .…”

Determination of early bone metastasis on Bone Scans Usingthe Gray Levels Histogram

“…An observational, retrospective, and analytic study was conducted in the Nuclear Medicine Department of the National Medical Center "La Raza" at IMSS [7] . The the image segmentation in near uniform regions [9] .…”

“…Several computer-assisted methods have been developed to find metastasis. Some techniques use the bone scan index [5] , neural networks [6] and false color [7] . All of them require a large number of bone scans datasets to calibrate the system, resulting in time-consuming procedures.…”