Image Area Reduction for Efficient Medical Image Retrieval

Content-based image retrieval (CBIR) has been one of the most active areas in medical image analysis in the last two decades because of the steadily increase in the number of digital images used. Efficient diagnosis and treatment planning can be supported by developing retrieval systems to provide high-quality healthcare. Extensive research has attempted to improve the image retrieval efficiency. The critical factors when searching in large databases are time and storage requirements. In general, although many methods have been suggested to increase accuracy, fast retrieval has been rather sporadically investigated. In this thesis, two different approaches are proposed to reduce both time and space requirements for medical image retrieval. The IRMA data set is used to validate the proposed methods. Both methods utilized Local Binary Pattern (LBP) histogram features which are extracted from 14,410 X-ray images of IRMA dataset. The first method is image folding that operates based on salient regions in an image. Saliency is determined by a context-aware saliency algorithm which includes folding the image. After the folding process, the reduced image area is used to extract multi-block and multi-scale LBP features and to classify these features by multi-class Support vector machine (SVM). The other method consists of classification and distance-based feature similarity. Images are firstly classified into general classes by utilizing LBP features. Subsequently, the retrieval is performed within the class to locate the most similar images. Between the retrieval and classification processes, LBP features are eliminated by employing the error histogram of a shallow (n/p/n) autoencoder to quantify the retrieval relevance of image blocks. If the region is relevant, the autoencoder gives large error for its decoding. Hence, via examining the autoencoder error of image blocks, irrelevant regions can be detected and eliminated. In order to calculate similarity within general classes, the distance between the LBP features of relevant regions is calculated. The results show that the retrieval time can be reduced, and the storage requirements can be lowered without significant decrease in accuracy

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians\u27 viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic. As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts\u27 eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts\u27 cognitive reasoning processes. The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts\u27 domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions. To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts\u27 sense-making

Descriptores geométricos en células plasmáticas usando un sistema en morfología digital hematológico en un hospital de Lima-Perú 2019

El presente trabajo hace referencia a descriptores geométricos de dos células: linfocitos normales y células plasmáticas; demostrando la variación que se interpreta numéricamente en las células plasmáticas en relación a los valores numéricos de los descriptores de linfocitos normales. Asimismo, descriptor geométrico es todo aquello que calcula y manifiesta las particularidades morfológicas tanto en la forma y el tamaño celular. Antes de llegar a los valores numéricos de cada uno de los descriptores estudiados, se realizó un hemograma automatizado y se reprocesó por microscopia óptica convencional para corroborar con el equipo automatizado. Se utilizó 20 casos, seleccionándose alrededor de entre 15 a 40 células plasmáticas por lámina estudiada. Por otra parte los datos examinados fueron valorados por un especialista en citomorfologia digital hematológica, asimismo la celulas plasmática fue escaneada utilizando el software VISION HEMA, lo cual bajo ciertos criterios algorítmicos manifestados en el progreso del trabajo se evidencio los valores expresados cuantitativamente, es decir en números por cada uno de los descriptores geométricos estudiados. Por ende se formó una base de datos para realizar la comparación numérica de la variación de cada descriptor geométrico entre una célula plasmática y un linfocito normal, dando como resultado un cuadro estadístico de provecho estudiado, evidenciando así toda variación a nivel numérica

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

Image analysis techniques for classification of pulmonary disease in cattle

Histologic analysis of tissue samples is often a critical step in the diagnosis of disease. However, this type of assessment is inherently subjective, and consequently a high degree of variability may occur between results produced by different pathologists. Histologic analysis is also a very time-consuming task for pathologists. Computer-based quantitative analysis of tissue samples shows promise for both reducing the subjectivity of traditional manual tissue assessments, as well as potentially reducing the time required to analyze each sample. The objective of this thesis project was to investigate image processing techniques and to develop software which could be used as a diagnostic aid in pathology assessments of cattle lung tissue samples. The software examines digital images of tissue samples, identifying and highlighting the presence of a set of features that indicate disease, and that can be used to distinguish various pulmonary diseases from one another. The output of the software is a series of segmented images with relevant disease indicators highlighted, and measurements quantifying the occurrence of these features within the tissue samples. Results of the software analysis of a set of 50 cattle lung tissue samples were compared to the detailed manual analysis of these samples by a pathology expert.The combination of image analysis techniques implemented in the thesis software shows potential. Detection of each of the disease indicators is successful to some extent, and in some cases the analysis results are extremely good. There is a large difference in accuracy rates for identification of the set of disease indicators, however, with sensitivity values ranging from a high of 94.8% to a low of 22.6%. This wide variation in result scores is partially due to limitations of the methodology used to determine accuracy

An Imaging Mass Spectrometry Investigation Into the N-linked Glycosylation Landscape of Pancreatic Ductal Adenocarcinoma and the Development of Associated Tools for Enhanced Glycan Separation and Characterization

The severity of pancreatic ductal adenocarcinoma (PDAC) is largely attributed to a failure to detect the disease before metastatic spread has occurred. CA19-9, a carbohydrate biomarker, is used clinically to surveille disease progression, but due to specificity challenges is not suitable for early discovery. As CA19-9 and other prospective markers are glycan epitopes, there is great clinical interest in understanding the glycobiology of pancreatic cancer. Unfortunately, few studies have been able to link glycosylation changes directly to pancreatic tumors and instead have focused on peripheral glycan alterations in the serum of PDAC patients. To address this gap in our understanding, we applied an imaging mass spectrometry (IMS) approach with complementary enzymatic and chemical isomer separation techniques to spatially assess the PDAC N-glycome in a cohort of pancreatic cancer patients. Orthogonally, we characterized the expression of CA19-9 and a new biomarker, sTRA, by multi-round immunofluorescence (IF) in the same cohort. These analyses revealed increased sialylation, fucosylation and branching amongst other structural themes in areas of PDAC tumor tissue. CA19-9 expressing tumors were defined by multiply branched, fucosylated bisecting N-glycans while sTRA expressing tumors favored tetraantennary N-glycans with polylactosamine extensions. IMS and IF-derived glycan and biomarker features were used to build classification models that detected PDAC tissue with an AUC of 0.939, outperforming models using either dataset individually. While studying sialylation isomers in our PDAC cohort, we saw an opportunity to enhance the chemical derivatization protocol we were using to address its shortcomings and expand its functionality. Subsequently, we developed a set of novel amidation-amidation strategies to stabilize and differentially label 2,3 and 2,6-linked sialic acids. In our alkyne-based approach, the differential mass shifts induced by the reactions allow for isomeric discrimination in imaging mass spectrometry experiments. This scheme, termed AAXL, was further characterized in clinical tissue specimens, biofluids and cultured cells. Our azide-based approach, termed AAN3, was more suitable for bioorthogonal applications, where the azide tag installed on 2,3 and 2,8-sialic acids could be reacted by click chemistry with a biotin-alkyne for subsequent streptavidin-peroxidase staining. Furthering the use of AAN3, we developed two additional techniques to fluorescently label (SAFER) and preferentially enrich (SABER) 2,3 and 2,8-linked sialic acids for more advanced glycomic applications. Initial experiments with these novel approaches have shown successful fluorescent staining and the identification of over 100 sialylated glycoproteins by LC-MS/MS. These four bioorthogonal strategies provide a new glycomic tool set for the characterization of sialic acid isomers in pancreatic and other cancers. Overall, this work furthers our collective understanding of the glycobiology underpinning pancreatic cancer and potentiates the discovery of novel carbohydrate biomarkers for the early detection of PDAC

Caracterització morfològica de cèl·lules limfoides normals, reactives, anormals i blàstiques de sang perifèrica mitjançant processament digital d'imatges

Aplicat embargament des de la data de defensa fins a l'abril de 2020The main objective of the present Doctoral Thesis is to obtain new quantitative features by means of digital image processing and machine learning for the differentiation of normal, reactive and malignant lymphoid cells of peripheral blood, contributing to an objective morphologic assessment.The research addresses the following two issues:(1)Using machine learning, geometric, color and texture descriptors are searched which have an explicit quantitative formulation and a reasonable qualitative interpretation in visual morphologic terms.(2)Considering cellular abnormalities established a priori, associated with specific diseases, the aim is to identify specific quantitative descriptors of morphological characteristics that cytologists recognize visually and usually express subjectively.More than 200 patients and 16 different lymphoid cell groups have been included in the research. Using the CellaVision DM96, 12,000 images have been acquired and using the microscope Olympus BX43, 9,000. Almost 2,700 features including geometric, color and texture (first and second-order statistics, granulometric, Wavelet and Gabor) have been analyzed for lymphoid cell differentiation. Six color spaces have been considered: RGB, CMYK, HSV, XYZ, Lab and Luv.The 20 most efficient features for the differentiation between reactive lymphoid cells (infections) and neoplastic cells (abnormal lymphocytes in lymphoma or lymphoid blasts in acute leukemia) have been analyzed. The three most relevant descriptors for the recognition of the 12 lymphoid cell groups considered are geometric: 1) nucleus/cytoplasm ratio, 2) nuclear perimeter and 3) cell diameter. Most of the 20 descriptors show significant differences between pairs of abnormal lymphocytes which are difficult to recognize by morphology. Five color and texture features are significant to discriminate reactive lymphocytes from abnormal lymphoid cells.Quantitative descriptors have been identified for the detection of specific cell morphologic abnormalities of certain lymphoid neoplasms, which have shown good specificity and sensitivity using the two different image acquisition systems. Regarding nuclear abnormalities, the detection of the mature and condensed chromatin seen in chronic lymphatic leukemia cells has been achieved by the correlation of the cyan of the nucleus, and the cerebriform chromatin characteristic of Sézary cells, by means of the standard deviation of the granulometric curve of the cyan component of the nucleus.Regarding the cytoplasm, hairiness descriptor has been able to detect cytoplasmic villi present in villous lymphocytes in hairy cell leukemia and splenic marginal zone lymphoma. The skewness of the histogram of the u component of the cytoplasm has shown to be useful for detecting azurophilic cytoplasmic granules seen in abnormal lymphocytes in T-cell large granular lymphocytic leukemia.The results of this Doctoral Thesis provide objectivity in the morphologic assessment of normal, reactive and neoplastic lymphoid cells. Obtaining quantitative descriptors for abnormal lymphoid cells, which are specific to certain lymphoid neoplasms with peripheral blood expression, could facilitate their detection. Hematological analyzers based on digital image analysis could benefit from the use of quantitative descriptors, such as those described herein, in order to discriminate between reactive and neoplastic lymphoid cells.L'objectiu general de la present Tesi és obtenir nous descriptors quantitatius mitjançant processament digital d’imatges i aprenentatge automàtic per a la diferenciació de cèl·lules limfoides normals, reactives i malignes de sang perifèrica, contribuint a una anàlisi objectiva de la citologia sanguínia. La recerca s'ha enfocat des de dues perspectives: (1) Partint de l'aprenentatge automàtic, s'han buscat descriptors geomètrics, de color i de textura que tinguin una formulació quantitativa explícita i una interpretació qualitativa raonable en termes morfològics visuals. (2) Partint d'anormalitats cel·lulars establertes a priori, associades a malalties específiques, l'objectiu és identificar descriptors quantitatius específics de característiques morfològiques que els citòlegs reconeixen de forma visual i expressen habitualment amb conceptes subjectius. S'han inclòs més de 200 pacients i 16 grups cel·lulars limfoides. Amb el sistema CellaVision DM96 s'han adquirit 12.000 imatges i amb el microscopi Olympus BX43, 9.000. Per a la diferenciació cel·lular s’han analitzat 2.700 descriptors geomètrics, de color i de textura (estadístics de primer i segon ordre, granulomètrics, Wavelet i Gabor). S'han considerat sis espais de color (RGB, CMGN, HSV, XYZ, Lab i Luv). S'han analitzat els 20 descriptors geomètrics, de color i de textura més eficients per a la diferenciació entre cèl·lules limfoides reactives (infeccions) i neoplàsiques (anormals als limfomes o blasts limfoides a les leucèmies agudes). Els descriptors més rellevants per al reconeixement dels 12 grups cel·lulars limfoides són geomètrics: 1) relació nucli/citoplasma, 2) perímetre del nucli i 3) diàmetre de la cèl·lula. La majoria dels 20 descriptors mostren diferències significatives entre parelles de limfòcits anormals de difícil reconeixement per morfologia. Cinc descriptors de color i textura són significatius per discriminar els limfòcits reactius dels anormals. S'han identificat descriptors quantitatius per a la detecció d'anomalies morfològiques específiques de cèl·lules de determinades neoplàsies limfoides, que han mostrat una bona especificitat i sensibilitat amb els dos sistemes diferents d’adquisició d’imatges. En relació a anormalitats nuclears, la detecció de la cromatina madura i condensada de les cèl·lules de la leucèmia limfàtica crònica ha estat possible mitjançant la correlació del cian del nucli, i de la cromatina cerebriforme característica de les cèl·lules de Sézary mitjançant la desviació estàndard de la corba granulomètrica del component cian del nucli. Pel que fa al citoplasma, s'han detectat les prolongacions citoplasmàtiques dels limfòcits de la tricoleucèmia i del limfoma de la zona marginal esplènic mitjançant el descriptor hairiness. L'asimetria de l'histograma del component u del citoplasma ha demostrat ser útil per detectar els grànuls azuròfils dels limfòcits grans granulars. Els resultats de la present Tesi proporcionen objectivitat en l'avaluació morfològica de cèl·lules limfoides normals, reactives i neoplàsiques. L'obtenció de descriptors quantitatius per cèl·lules limfoides anormals específiques de determinades neoplàsies limfoides amb expressió a sang perifèrica podria facilitar la seva detecció. Els analitzadors hematològics basats en l'anàlisi digital d'imatges es podrien beneficiar de l'ús de descriptors quantitatius, com els descrits, per tal de discriminar entre cèl·lules limfoides reactives i neoplàsiques.Postprint (published version

Caracterització morfològica de cèl·lules limfoides normals, reactives, anormals i blàstiques de sang perifèrica mitjançant processament digital d'imatges

The main objective of the present Doctoral Thesis is to obtain new quantitative features by means of digital image processing and machine learning for the differentiation of normal, reactive and malignant lymphoid cells of peripheral blood, contributing to an objective morphologic assessment.The research addresses the following two issues:(1)Using machine learning, geometric, color and texture descriptors are searched which have an explicit quantitative formulation and a reasonable qualitative interpretation in visual morphologic terms.(2)Considering cellular abnormalities established a priori, associated with specific diseases, the aim is to identify specific quantitative descriptors of morphological characteristics that cytologists recognize visually and usually express subjectively.More than 200 patients and 16 different lymphoid cell groups have been included in the research. Using the CellaVision DM96, 12,000 images have been acquired and using the microscope Olympus BX43, 9,000. Almost 2,700 features including geometric, color and texture (first and second-order statistics, granulometric, Wavelet and Gabor) have been analyzed for lymphoid cell differentiation. Six color spaces have been considered: RGB, CMYK, HSV, XYZ, Lab and Luv.The 20 most efficient features for the differentiation between reactive lymphoid cells (infections) and neoplastic cells (abnormal lymphocytes in lymphoma or lymphoid blasts in acute leukemia) have been analyzed. The three most relevant descriptors for the recognition of the 12 lymphoid cell groups considered are geometric: 1) nucleus/cytoplasm ratio, 2) nuclear perimeter and 3) cell diameter. Most of the 20 descriptors show significant differences between pairs of abnormal lymphocytes which are difficult to recognize by morphology. Five color and texture features are significant to discriminate reactive lymphocytes from abnormal lymphoid cells.Quantitative descriptors have been identified for the detection of specific cell morphologic abnormalities of certain lymphoid neoplasms, which have shown good specificity and sensitivity using the two different image acquisition systems. Regarding nuclear abnormalities, the detection of the mature and condensed chromatin seen in chronic lymphatic leukemia cells has been achieved by the correlation of the cyan of the nucleus, and the cerebriform chromatin characteristic of Sézary cells, by means of the standard deviation of the granulometric curve of the cyan component of the nucleus.Regarding the cytoplasm, hairiness descriptor has been able to detect cytoplasmic villi present in villous lymphocytes in hairy cell leukemia and splenic marginal zone lymphoma. The skewness of the histogram of the u component of the cytoplasm has shown to be useful for detecting azurophilic cytoplasmic granules seen in abnormal lymphocytes in T-cell large granular lymphocytic leukemia.The results of this Doctoral Thesis provide objectivity in the morphologic assessment of normal, reactive and neoplastic lymphoid cells. Obtaining quantitative descriptors for abnormal lymphoid cells, which are specific to certain lymphoid neoplasms with peripheral blood expression, could facilitate their detection. Hematological analyzers based on digital image analysis could benefit from the use of quantitative descriptors, such as those described herein, in order to discriminate between reactive and neoplastic lymphoid cells.L'objectiu general de la present Tesi és obtenir nous descriptors quantitatius mitjançant processament digital d’imatges i aprenentatge automàtic per a la diferenciació de cèl·lules limfoides normals, reactives i malignes de sang perifèrica, contribuint a una anàlisi objectiva de la citologia sanguínia. La recerca s'ha enfocat des de dues perspectives: (1) Partint de l'aprenentatge automàtic, s'han buscat descriptors geomètrics, de color i de textura que tinguin una formulació quantitativa explícita i una interpretació qualitativa raonable en termes morfològics visuals. (2) Partint d'anormalitats cel·lulars establertes a priori, associades a malalties específiques, l'objectiu és identificar descriptors quantitatius específics de característiques morfològiques que els citòlegs reconeixen de forma visual i expressen habitualment amb conceptes subjectius. S'han inclòs més de 200 pacients i 16 grups cel·lulars limfoides. Amb el sistema CellaVision DM96 s'han adquirit 12.000 imatges i amb el microscopi Olympus BX43, 9.000. Per a la diferenciació cel·lular s’han analitzat 2.700 descriptors geomètrics, de color i de textura (estadístics de primer i segon ordre, granulomètrics, Wavelet i Gabor). S'han considerat sis espais de color (RGB, CMGN, HSV, XYZ, Lab i Luv). S'han analitzat els 20 descriptors geomètrics, de color i de textura més eficients per a la diferenciació entre cèl·lules limfoides reactives (infeccions) i neoplàsiques (anormals als limfomes o blasts limfoides a les leucèmies agudes). Els descriptors més rellevants per al reconeixement dels 12 grups cel·lulars limfoides són geomètrics: 1) relació nucli/citoplasma, 2) perímetre del nucli i 3) diàmetre de la cèl·lula. La majoria dels 20 descriptors mostren diferències significatives entre parelles de limfòcits anormals de difícil reconeixement per morfologia. Cinc descriptors de color i textura són significatius per discriminar els limfòcits reactius dels anormals. S'han identificat descriptors quantitatius per a la detecció d'anomalies morfològiques específiques de cèl·lules de determinades neoplàsies limfoides, que han mostrat una bona especificitat i sensibilitat amb els dos sistemes diferents d’adquisició d’imatges. En relació a anormalitats nuclears, la detecció de la cromatina madura i condensada de les cèl·lules de la leucèmia limfàtica crònica ha estat possible mitjançant la correlació del cian del nucli, i de la cromatina cerebriforme característica de les cèl·lules de Sézary mitjançant la desviació estàndard de la corba granulomètrica del component cian del nucli. Pel que fa al citoplasma, s'han detectat les prolongacions citoplasmàtiques dels limfòcits de la tricoleucèmia i del limfoma de la zona marginal esplènic mitjançant el descriptor hairiness. L'asimetria de l'histograma del component u del citoplasma ha demostrat ser útil per detectar els grànuls azuròfils dels limfòcits grans granulars. Els resultats de la present Tesi proporcionen objectivitat en l'avaluació morfològica de cèl·lules limfoides normals, reactives i neoplàsiques. L'obtenció de descriptors quantitatius per cèl·lules limfoides anormals específiques de determinades neoplàsies limfoides amb expressió a sang perifèrica podria facilitar la seva detecció. Els analitzadors hematològics basats en l'anàlisi digital d'imatges es podrien beneficiar de l'ús de descriptors quantitatius, com els descrits, per tal de discriminar entre cèl·lules limfoides reactives i neoplàsiques