Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets

The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method

Three-level Local Thresholding Berbasis Metode Otsu Untuk Segmentasi Leukosit Pada Citra Leukemia Limfoblastik Akut

. Segmentation of Acute Lymphoblastic Leukemia (ALL) images can be used to identify the presence of ALL disease. In this paper, three-level local thresholdings based on Otsu method is presented for leucocytes segmentation in ALL image. Firstly, a method based on Gram-Schmidt orthogonalization theory is applied to partition the input image into several sub-images. The proposed method extends Otsu’s bi-level thresholding to three-level thresholding method to find two local threshold values that maximize between-class variance. Using the two local threshold values and three-level local thresholding technique then segmenting each of sub-images into three regions, e.g. nucleus, cytoplasm, and background. To evaluate the performance of the proposed method, 32 peripheral blood smear images are used. The performance of the proposed method is compared with manually segmented ground truth using Zijdenbos similarity index (ZSI), precision, and recall. An experimental evaluation demonstrates superior performance over three-level global thresholding for ALL image segmentation

DeepSegNet: An Innovative Framework for Accurate Blood Cell Image Segmentation

Image segmentation plays a crucial and indispensable role in computer vision, as it allows the partitioning of an image into meaningful regions or objects. Among its numerous applications, image segmentation holds particular significance in the domains of medical diagnosis and healthcare. Its vital role in this field stems from its ability to extract and delineate specific anatomical structures, tumors, lesions, and other critical regions from medical images. In medical diagnosis, accurate and precise segmentation of organs and abnormalities is paramount for effective treatment planning, disease monitoring, and surgical interventions. Blood cell image segmentation is highly valuable for medical diagnosis and research, particularly in the domains of hematology and pathology. Precisely segmenting blood cells from microscopic images is essential, as it offers critical insights into various blood-related disorders and diseases. Although deep learning segmentation models have exhibited promising results in blood cell image segmentation, they suffer from several limitations. These drawbacks encompass scarce data availability, inefficient feature extraction, extended computation time, limited generalization to unseen data, challenges with variations, and artifacts. Consequently, these limitations can adversely impact the overall performance of the models. Blood cell image segmentation encounters persistent challenges due to factors like irregular cell shapes, which pose difficulties in boundary delineation, imperfect cell separation in smears, and low cell contrast, leading to visibility issues during segmentation. This research article introduces the innovative DeepSegNet framework, a powerful solution for precise blood cell image segmentation. The performance of widely-used segmentation models like PSPNet, FPN, and DeepLabv3+ is enhanced through the use of sophisticated preprocessing techniques, improving generalization capability, data diversity, and training stability. Additionally, the incorporation of diverse dilated convolutions and feature fusion further contributes to the improvement of these models. The Improved PSPNet, Improved FPN, Deep Lab V3, and Improved Deep Lab V3+ achieved 98.25%, 99.04%, 98.23%, and 99.31% accuracy, respectively, and the Improved Deep Lab V3+ model outperformed well and produced a Dice Coefficient of 99.32% and Precision of 99.38%. The proposed DeepSegNet framework improves overall performance with an increased accuracy of 8.91%, 3.72%, 17.73%, 22.83%, 7.96%, 9.61%, 17.36%, 6.22%, 13.32%, and 14.32% compared to the existing models. This framework, which can be applied to accurately identify and quantify different cell types from blood cell images, is instrumental in diagnosing a variety of hematological disorders and diseases

PENGOLAHAN CITRA UNTUK MEMISAHKAN DAN MENGHITUNG JUMLAH SEL LEUKEMIA PADA CITRA MIKROSKOPIS SEL DARAH DENGAN VARIASI WARNA DAN SKALA UKURAN SEL

Abstrak. Pada bidang medis, analisis citra mikroskopis sel darah merupakan suatu hal yang penting untuk mendiagnosa penyakit yang sedang diderita oleh pasien seperti leukemia. Pada citra mikroskopis sel darah yang berisi banyak sel leukemia, kadang kala posisi sel leukemia saling berhimpitan satu dengan yang lainnya. Hal ini akan menyulitkan dalam melakukan identifikasi jenis sel leukemia karena sel yang berhimpitan tersebut akan dianggap sebagai satu bagian sel. Oleh karena itu, pada penelitian ini akan dilakukan pemisahan sel leukemia yang berhimpitan dan penghitungan jumlah sel leukemia secara otomatis dari hasil segmentasi citra sel leukemia berwarna yang fleksibel terhadap variasi warna dan skala ukuran sel. Hasil dari penelitian ini adalah (1) segmentasi citra sel leukemia otomatis dengan variasi warna dan skala ukuran sel mempunyai akurasi sebesar 94%. (2) Identifikasi sel yang berhimpit dapat dilakukan dengan melakukan uji roundness, dengan nilai ambang 0.7. (3) Pemisahan sel leukemia yang berhimpit dapat dilakukan dengan menggunakan algoritma watershed. (4) Penghitungan jumlah sel dilakukan berdasarkan hasil pemisahan sel menjadi sel leukemia tunggal. (5) Pembuatan sub-citra yang berisi sel tunggal dilakukan berdasarkan jumlah sel yang diperoleh serta dengan membuat bounding box dari citra sel leukemia yang telah dipisahkan.   Kata Kunci: Bounding box, clustering, distance transform, segmentasi sel darah putih, watershe

Medical Image Segmentation: Thresholding and Minimum Spanning Trees

I bildesegmentering deles et bilde i separate objekter eller regioner. Det er et essensielt skritt i bildebehandling for å definere interesseområder for videre behandling eller analyse. Oppdelingsprosessen reduserer kompleksiteten til et bilde for å forenkle analysen av attributtene oppnådd etter segmentering. Det forandrer representasjonen av informasjonen i det opprinnelige bildet og presenterer pikslene på en måte som er mer meningsfull og lettere å forstå. Bildesegmentering har forskjellige anvendelser. For medisinske bilder tar segmenteringsprosessen sikte på å trekke ut bildedatasettet for å identifisere områder av anatomien som er relevante for en bestemt studie eller diagnose av pasienten. For eksempel kan man lokalisere berørte eller anormale deler av kroppen. Segmentering av oppfølgingsdata og baseline lesjonssegmentering er også svært viktig for å vurdere behandlingsresponsen. Det er forskjellige metoder som blir brukt for bildesegmentering. De kan klassifiseres basert på hvordan de er formulert og hvordan segmenteringsprosessen utføres. Metodene inkluderer de som er baserte på terskelverdier, graf-baserte, kant-baserte, klynge-baserte, modell-baserte og hybride metoder, og metoder basert på maskinlæring og dyp læring. Andre metoder er baserte på å utvide, splitte og legge sammen regioner, å finne diskontinuiteter i randen, vannskille segmentering, aktive kontuter og graf-baserte metoder. I denne avhandlingen har vi utviklet metoder for å segmentere forskjellige typer medisinske bilder. Vi testet metodene på datasett for hvite blodceller (WBCs) og magnetiske resonansbilder (MRI). De utviklede metodene og analysen som er utført på bildedatasettet er presentert i tre artikler. I artikkel A (Paper A) foreslo vi en metode for segmentering av nukleuser og cytoplasma fra hvite blodceller. Metodene estimerer terskelen for segmentering av nukleuser automatisk basert på lokale minima. Metoden segmenterer WBC-ene før segmentering av cytoplasma avhengig av kompleksiteten til objektene i bildet. For bilder der WBC-ene er godt skilt fra røde blodlegemer (RBC), er WBC-ene segmentert ved å ta gjennomsnittet av $n$ bilder som allerede var filtrert med en terskelverdi. For bilder der RBC-er overlapper WBC-ene, er hele WBC-ene segmentert ved hjelp av enkle lineære iterative klynger (SLIC) og vannskillemetoder. Cytoplasmaet oppnås ved å trekke den segmenterte nukleusen fra den segmenterte WBC-en. Metoden testes på to forskjellige offentlig tilgjengelige datasett, og resultatene sammenlignes med toppmoderne metoder. I artikkel B (Paper B) foreslo vi en metode for segmentering av hjernesvulster basert på minste dekkende tre-konsepter (minimum spanning tree, MST). Metoden utfører interaktiv segmentering basert på MST. I denne artikkelen er bildet lastet inn i et interaktivt vindu for segmentering av svulsten. Fokusregion og bakgrunn skilles ved å klikke for å dele MST i to trær. Ett av disse trærne representerer fokusregionen og det andre representerer bakgrunnen. Den foreslåtte metoden ble testet ved å segmentere to forskjellige 2D-hjerne T1 vektede magnetisk resonans bildedatasett. Metoden er enkel å implementere og resultatene indikerer at den er nøyaktig og effektiv. I artikkel C (Paper C) foreslår vi en metode som behandler et 3D MRI-volum og deler det i hjernen, ikke-hjernevev og bakgrunnsegmenter. Det er en grafbasert metode som bruker MST til å skille 3D MRI inn i de tre regiontypene. Grafen lages av et forhåndsbehandlet 3D MRI-volum etterfulgt av konstrueringen av MST-en. Segmenteringsprosessen gir tre merkede, sammenkoblende komponenter som omformes tilbake til 3D MRI-form. Etikettene brukes til å segmentere hjernen, ikke-hjernevev og bakgrunn. Metoden ble testet på tre forskjellige offentlig tilgjengelige datasett og resultatene ble sammenlignet med ulike toppmoderne metoder.In image segmentation, an image is divided into separate objects or regions. It is an essential step in image processing to define areas of interest for further processing or analysis. The segmentation process reduces the complexity of an image to simplify the analysis of the attributes obtained after segmentation. It changes the representation of the information in the original image and presents the pixels in a way that is more meaningful and easier to understand. Image segmentation has various applications. For medical images, the segmentation process aims to extract the image data set to identify areas of the anatomy relevant to a particular study or diagnosis of the patient. For example, one can locate affected or abnormal parts of the body. Segmentation of follow-up data and baseline lesion segmentation is also very important to assess the treatment response. There are different methods used for image segmentation. They can be classified based on how they are formulated and how the segmentation process is performed. The methods include those based on threshold values, edge-based, cluster-based, model-based and hybrid methods, and methods based on machine learning and deep learning. Other methods are based on growing, splitting and merging regions, finding discontinuities in the edge, watershed segmentation, active contours and graph-based methods. In this thesis, we have developed methods for segmenting different types of medical images. We tested the methods on datasets for white blood cells (WBCs) and magnetic resonance images (MRI). The developed methods and the analysis performed on the image data set are presented in three articles. In Paper A we proposed a method for segmenting nuclei and cytoplasm from white blood cells. The method estimates the threshold for segmentation of nuclei automatically based on local minima. The method segments the WBCs before segmenting the cytoplasm depending on the complexity of the objects in the image. For images where the WBCs are well separated from red blood cells (RBCs), the WBCs are segmented by taking the average of $n$ images that were already filtered with a threshold value. For images where RBCs overlap the WBCs, the entire WBCs are segmented using simple linear iterative clustering (SLIC) and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC. The method is tested on two different publicly available datasets, and the results are compared with state of the art methods. In Paper B, we proposed a method for segmenting brain tumors based on minimum spanning tree (MST) concepts. The method performs interactive segmentation based on the MST. In this paper, the image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the MST into two trees. One of these trees represents the region of interest and the other represents the background. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The method is simple to implement and the results indicate that it is accurate and efficient. In Paper C, we propose a method that processes a 3D MRI volume and partitions it into brain, non-brain tissues, and background segments. It is a graph-based method that uses MST to separate the 3D MRI into the brain, non-brain, and background regions. The graph is made from a preprocessed 3D MRI volume followed by constructing the MST. The segmentation process produces three labeled connected components which are reshaped back to the shape of the 3D MRI. The labels are used to segment the brain, non-brain tissues, and the background. The method was tested on three different publicly available data sets and the results were compared to different state of the art methods.Doktorgradsavhandlin

SEGMENTASI DAN PEMISAHAN SEL DARAH PUTIH BERSENTUHAN MENGGUNAKAN K-MEANS DAN HIERARCHICAL CLUSTERING ANALYSIS PADA CITRA LEUKEMIA MYELOID AKUT

The success of identification and classification on diagnosing acute myeloid leukemia (AML) diseases based on image processing relies heavily on segmentation result. Segmentation on peripheral blood smear images aims to separate the leukocytes region with others region. To increase the segmentation accuracy on AML images, a few things regarding lighting condition, contrast, staining variations and the existence of touching cells must be overcome. In this study a method for leukocytes segmentation and separate the touching cell on AML images using cluster analysis with K-Means and hierarchical clustering analysis (HCA) is proposed. K-Means method is used to analyze the cluster for AML images segmentation. The AML image datasets with various staining variations is segmented using K-Means method.  The existence of touching cells is separated using HCA method which produce a stable clusters result. Segmentation and cell separation will be processed on local region or sub-image which is obtained from AML images cropping. From the evaluation results in 40 images of AML dataset, the proposed method is capable to properly segment the white blood cells region and separating the touching cell into a single cells. The average value of the segmentation results is 0.977 for precision, 0.885 for recall and 0.928 for Zijdenbos similarity index (ZSI) in white blood cell region. While in nucleus region the average value is 0.975 for precision, 0.924 for recall and 0.948 for ZSI. On cell counting, the error rate is also low which about 7.68%