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Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter\u2010 and intraobserver variability. The present work introduces an image combined\u2010descriptor to detect blasts and determine their probable lineage. This strategy uses an intra\u2010nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner\u2010nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically\u2010segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co\u2010occurrence matrix representation. Both InMoP and Haralick\u2010based descriptors are calculated using the b\u2010channel from Lab color\u2010space. The combined\u2010descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database\u2010D1 (\n                    <jats:italic>n =<\/jats:italic>\n                    260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database\u2010D2 contains acute myeloid leukemia (AML) blasts (\n                    <jats:italic>n =<\/jats:italic>\n                    3294) and nonblast (\n                    <jats:italic>n =<\/jats:italic>\n                    15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (\n                    <jats:italic>n =<\/jats:italic>\n                    6588) and testing in D1 (\n                    <jats:italic>n =<\/jats:italic>\n                    260), obtaining a training AUC of 0.991\u2009\u00b1\u20090.002 and AUC\u2009=\u20090.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state\u2010of\u2010the\u2010art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1\u2010score.\n                  <\/jats:p>","DOI":"10.1002\/cyto.a.24785","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:08:31Z","timestamp":1691748511000},"page":"857-867","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intra\u2010nucleus mosaic pattern (\n                    <scp>InMop<\/scp>\n                    ) and whole\u2010cell Haralick combined\u2010descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images"],"prefix":"10.1002","volume":"103","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1612-721X","authenticated-orcid":false,"given":"Jonathan","family":"Tarquino","sequence":"first","affiliation":[{"name":"Computer Imaging and Medical Application Laboratory Universidad Nacional de Colombia Bogot\u00e1 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