Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.

BACKGROUND: Morphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast. METHODS: Fifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell. RESULTS: Three binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0). CONCLUSIONS: The CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.

Quantitative Distinction of Morphological Characteristics of White Blood Cells in Peripheral Blood Through Texture Analysis Using Gray Level Co-Occurrence Matrix.

BACKGROUND: Texture features are valuable clues for skilled technicians to differentiate peripheral blood (PB) white blood cells (WBCs). Some studies have tried to distinguish WBCs automatically by using texture analysis. However, no study so far has applied a gray level co-occurrence matrix (GLCM) to images of PB WBCs. Here, we developed a new GLCM method, called the CM-Tx method, for automatically distinguishing PB WBCs. METHODS: We used a total of 199 images of six different types of PB WBCs, taken from PB smears of 12 healthy volunteers, as objective standard images for the analysis. The six types were band form neutrophil, segmented form neutrophil, eosinophil, basophil, lymphocyte, and monocyte. Using in-house FORTRAN programs, three types of GLCM (R: distance (d) = 1, direction (θ) = 0°), (U: d = 1, θ = 270°) and (AE: d = 1, θ = 15° x q: q = 0, ..., 23), the mean intensity (MI) of each image and nine different texture distinction indexes (TDIs) for each GLCM were calculated. Then, a threshold value (TV) for distinguishing the type of PB WBC was selected from the dot plots of all TDIs and the MI. RESULTS: In total, we made 1,194 GLCMs. Using the selected TVs of the TDI, four sequential binary divisions could distinguish five types of PB WBCs. First, monocytes were distinguished (sensitivity 100%, specificity 100%, p < 0.0001) with the TV of the inverse difference moment of the GLCM (U). Then, segmented and band form neutrophils were distinguished from the remaining (100%, 99%, p < 0.0001) with the TV of the contrast of the GLCM (AE). Next, lymphocytes were distinguished (100%, 98%, p < 0.0001) with the TV of the entropy of the GLCM (AE). Finally, basophils were distinguished (82.4%, 100%, p < 0.0001) from eosinophils with the TV of the summed entropy of the GLCM (R). Band form neutrophils could not be distinguished from segmented form neutrophils. The average sensitivity of the CM-Tx method for the five types was 95.6%, and its average specificity was 99.3%. CONCLUSIONS: The CM-Tx method can distinguish five types of PB WBCs by using numerical differences only in texture futures quantified with GLCM. However, some other method was needed to distinguish the band and segmented form neutrophils from each other.

Semi-Automatic Rating Method for Neutrophil Alkaline Phosphatase Activity.

BACKGROUND: The neutrophil alkaline phosphatase (NAP) score is a valuable test for the diagnosis of myeloproliferative neoplasms, but it has still manually rated. Therefore, we developed a semi-automatic rating method using Photoshop® and Image-J, called NAP-PS-IJ. METHODS: Neutrophil alkaline phosphatase staining was conducted with Tomonaga's method to films of peripheral blood taken from three healthy volunteers. At least 30 neutrophils with NAP scores from 0 to 5+ were observed and taken their images. From which the outer part of neutrophil was removed away with Image-J. These were binarized with two different procedures (P1 and P2) using Photoshop® . NAP-positive area (NAP-PA) and granule (NAP-PGC) were measured and counted with Image-J. RESULTS: The NAP-PA in images binarized with P1 significantly (P < 0.05) differed between images with NAP scores from 0 to 3+ (group 1) and those from 4+ to 5+ (group 2). The original images in group 1 were binarized with P2. NAP-PGC of them significantly (P < 0.05) differed among all four NAP score groups. The mean NAP-PGC with NAP-PS-IJ indicated a good correlation (r = 0.92, P < 0.001) to results by human examiners. CONCLUSIONS: The sensitivity and specificity of NAP-PS-IJ were 60% and 92%, which might be considered as a prototypic method for the full-automatic rating NAP score.